---
_id: '6944'
abstract:
- lang: eng
text: 'We study the problem of automatically detecting if a given multi-class classifier
operates outside of its specifications (out-of-specs), i.e. on input data from
a different distribution than what it was trained for. This is an important problem
to solve on the road towards creating reliable computer vision systems for real-world
applications, because the quality of a classifier’s predictions cannot be guaranteed
if it operates out-of-specs. Previously proposed methods for out-of-specs detection
make decisions on the level of single inputs. This, however, is insufficient to
achieve low false positive rate and high false negative rates at the same time.
In this work, we describe a new procedure named KS(conf), based on statistical
reasoning. Its main component is a classical Kolmogorov–Smirnov test that is applied
to the set of predicted confidence values for batches of samples. Working with
batches instead of single samples allows increasing the true positive rate without
negatively affecting the false positive rate, thereby overcoming a crucial limitation
of single sample tests. We show by extensive experiments using a variety of convolutional
network architectures and datasets that KS(conf) reliably detects out-of-specs
situations even under conditions where other tests fail. It furthermore has a
number of properties that make it an excellent candidate for practical deployment:
it is easy to implement, adds almost no overhead to the system, works with any
classifier that outputs confidence scores, and requires no a priori knowledge
about how the data distribution could change.'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Rémy
full_name: Sun, Rémy
last_name: Sun
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sun R, Lampert C. KS(conf): A light-weight test if a multiclass classifier
operates outside of its specifications. International Journal of Computer Vision.
2020;128(4):970-995. doi:10.1007/s11263-019-01232-x'
apa: 'Sun, R., & Lampert, C. (2020). KS(conf): A light-weight test if a multiclass
classifier operates outside of its specifications. International Journal of
Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01232-x'
chicago: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a
Multiclass Classifier Operates Outside of Its Specifications.” International
Journal of Computer Vision. Springer Nature, 2020. https://doi.org/10.1007/s11263-019-01232-x.'
ieee: 'R. Sun and C. Lampert, “KS(conf): A light-weight test if a multiclass classifier
operates outside of its specifications,” International Journal of Computer
Vision, vol. 128, no. 4. Springer Nature, pp. 970–995, 2020.'
ista: 'Sun R, Lampert C. 2020. KS(conf): A light-weight test if a multiclass classifier
operates outside of its specifications. International Journal of Computer Vision.
128(4), 970–995.'
mla: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a Multiclass
Classifier Operates Outside of Its Specifications.” International Journal of
Computer Vision, vol. 128, no. 4, Springer Nature, 2020, pp. 970–95, doi:10.1007/s11263-019-01232-x.'
short: R. Sun, C. Lampert, International Journal of Computer Vision 128 (2020) 970–995.
date_created: 2019-10-14T09:14:28Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2024-02-22T14:57:30Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.1007/s11263-019-01232-x
ec_funded: 1
external_id:
isi:
- '000494406800001'
file:
- access_level: open_access
checksum: 155e63edf664dcacb3bdc1c2223e606f
content_type: application/pdf
creator: dernst
date_created: 2019-11-26T10:30:02Z
date_updated: 2020-07-14T12:47:45Z
file_id: '7110'
file_name: 2019_IJCV_Sun.pdf
file_size: 1715072
relation: main_file
file_date_updated: 2020-07-14T12:47:45Z
has_accepted_license: '1'
intvolume: ' 128'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 970-995
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1007/s11263-019-01262-5
record:
- id: '6482'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: 'KS(conf): A light-weight test if a multiclass classifier operates outside
of its specifications'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 128
year: '2020'
...
---
_id: '6482'
abstract:
- lang: eng
text: 'Computer vision systems for automatic image categorization have become accurate
and reliable enough that they can run continuously for days or even years as components
of real-world commercial applications. A major open problem in this context, however,
is quality control. Good classification performance can only be expected if systems
run under the specific conditions, in particular data distributions, that they
were trained for. Surprisingly, none of the currently used deep network architectures
have a built-in functionality that could detect if a network operates on data
from a distribution it was not trained for, such that potentially a warning to
the human users could be triggered. In this work, we describe KS(conf), a procedure
for detecting such outside of specifications (out-of-specs) operation, based on
statistical testing of the network outputs. We show by extensive experiments using
the ImageNet, AwA2 and DAVIS datasets on a variety of ConvNets architectures that
KS(conf) reliably detects out-of-specs situations. It furthermore has a number
of properties that make it a promising candidate for practical deployment: it
is easy to implement, adds almost no overhead to the system, works with all networks,
including pretrained ones, and requires no a priori knowledge of how the data
distribution could change. '
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Rémy
full_name: Sun, Rémy
last_name: Sun
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sun R, Lampert C. KS(conf): A light-weight test if a ConvNet operates outside
of Its specifications. In: Vol 11269. Springer Nature; 2019:244-259. doi:10.1007/978-3-030-12939-2_18'
apa: 'Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a ConvNet
operates outside of Its specifications (Vol. 11269, pp. 244–259). Presented at
the GCPR: Conference on Pattern Recognition, Stuttgart, Germany: Springer Nature.
https://doi.org/10.1007/978-3-030-12939-2_18'
chicago: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a
ConvNet Operates Outside of Its Specifications,” 11269:244–59. Springer Nature,
2019. https://doi.org/10.1007/978-3-030-12939-2_18.'
ieee: 'R. Sun and C. Lampert, “KS(conf): A light-weight test if a ConvNet operates
outside of Its specifications,” presented at the GCPR: Conference on Pattern Recognition,
Stuttgart, Germany, 2019, vol. 11269, pp. 244–259.'
ista: 'Sun R, Lampert C. 2019. KS(conf): A light-weight test if a ConvNet operates
outside of Its specifications. GCPR: Conference on Pattern Recognition, LNCS,
vol. 11269, 244–259.'
mla: 'Sun, Rémy, and Christoph Lampert. KS(Conf): A Light-Weight Test If a ConvNet
Operates Outside of Its Specifications. Vol. 11269, Springer Nature, 2019,
pp. 244–59, doi:10.1007/978-3-030-12939-2_18.'
short: R. Sun, C. Lampert, in:, Springer Nature, 2019, pp. 244–259.
conference:
end_date: 2018-10-12
location: Stuttgart, Germany
name: 'GCPR: Conference on Pattern Recognition'
start_date: 2018-10-09
date_created: 2019-05-24T09:48:36Z
date_published: 2019-02-14T00:00:00Z
date_updated: 2024-02-22T14:57:29Z
day: '14'
department:
- _id: ChLa
doi: 10.1007/978-3-030-12939-2_18
ec_funded: 1
external_id:
arxiv:
- '1804.04171'
intvolume: ' 11269'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1804.04171
month: '02'
oa: 1
oa_version: Preprint
page: 244-259
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783030129385'
- '9783030129392'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '6944'
relation: later_version
status: public
scopus_import: '1'
status: public
title: 'KS(conf): A light-weight test if a ConvNet operates outside of Its specifications'
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 11269
year: '2019'
...
---
_id: '6590'
abstract:
- lang: eng
text: 'Modern machine learning methods often require more data for training than
a single expert can provide. Therefore, it has become a standard procedure to
collect data from external sources, e.g. via crowdsourcing. Unfortunately, the
quality of these sources is not always guaranteed. As additional complications,
the data might be stored in a distributed way, or might even have to remain private.
In this work, we address the question of how to learn robustly in such scenarios.
Studying the problem through the lens of statistical learning theory, we derive
a procedure that allows for learning from all available sources, yet automatically
suppresses irrelevant or corrupted data. We show by extensive experiments that
our method provides significant improvements over alternative approaches from
robust statistics and distributed optimization. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Konstantinov NH, Lampert C. Robust learning from untrusted sources. In: Proceedings
of the 36th International Conference on Machine Learning. Vol 97. ML Research
Press; 2019:3488-3498.'
apa: 'Konstantinov, N. H., & Lampert, C. (2019). Robust learning from untrusted
sources. In Proceedings of the 36th International Conference on Machine Learning
(Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.'
chicago: Konstantinov, Nikola H, and Christoph Lampert. “Robust Learning from Untrusted
Sources.” In Proceedings of the 36th International Conference on Machine Learning,
97:3488–98. ML Research Press, 2019.
ieee: N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,”
in Proceedings of the 36th International Conference on Machine Learning,
Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498.
ista: 'Konstantinov NH, Lampert C. 2019. Robust learning from untrusted sources.
Proceedings of the 36th International Conference on Machine Learning. ICML: International
Conference on Machine Learning vol. 97, 3488–3498.'
mla: Konstantinov, Nikola H., and Christoph Lampert. “Robust Learning from Untrusted
Sources.” Proceedings of the 36th International Conference on Machine Learning,
vol. 97, ML Research Press, 2019, pp. 3488–98.
short: N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International
Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498.
conference:
end_date: 2919-06-15
location: Long Beach, CA, USA
name: 'ICML: International Conference on Machine Learning'
start_date: 2019-06-10
date_created: 2019-06-27T14:18:23Z
date_published: 2019-06-01T00:00:00Z
date_updated: 2023-10-17T12:31:55Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1901.10310'
intvolume: ' 97'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1901.10310
month: '06'
oa: 1
oa_version: Preprint
page: 3488-3498
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 36th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
record:
- id: '10799'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Robust learning from untrusted sources
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 97
year: '2019'
...
---
_id: '7479'
abstract:
- lang: eng
text: "Multi-exit architectures, in which a stack of processing layers is interleaved
with early output layers, allow the processing of a test example to stop early
and thus save computation time and/or energy. In this work, we propose a new
training procedure for multi-exit architectures based on the principle of knowledge
distillation. The method encourage searly exits to mimic later, more accurate
exits, by matching their output probabilities.\r\nExperiments on CIFAR100 and
\ ImageNet show that distillation-based training significantly improves the
accuracy of early exits while maintaining state-of-the-art accuracy for late
\ ones. The method is particularly beneficial when training data is limited
\ and it allows a straightforward extension to semi-supervised learning,i.e.
making use of unlabeled data at training time. Moreover, it takes only afew lines
to implement and incurs almost no computational overhead at training time, and
none at all at test time."
article_processing_charge: No
author:
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Phuong M, Lampert C. Distillation-based training for multi-exit architectures.
In: IEEE International Conference on Computer Vision. Vol 2019-October.
IEEE; 2019:1355-1364. doi:10.1109/ICCV.2019.00144'
apa: 'Phuong, M., & Lampert, C. (2019). Distillation-based training for multi-exit
architectures. In IEEE International Conference on Computer Vision (Vol.
2019–October, pp. 1355–1364). Seoul, Korea: IEEE. https://doi.org/10.1109/ICCV.2019.00144'
chicago: Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit
Architectures.” In IEEE International Conference on Computer Vision, 2019–October:1355–64.
IEEE, 2019. https://doi.org/10.1109/ICCV.2019.00144.
ieee: M. Phuong and C. Lampert, “Distillation-based training for multi-exit architectures,”
in IEEE International Conference on Computer Vision, Seoul, Korea, 2019,
vol. 2019–October, pp. 1355–1364.
ista: 'Phuong M, Lampert C. 2019. Distillation-based training for multi-exit architectures.
IEEE International Conference on Computer Vision. ICCV: International Conference
on Computer Vision vol. 2019–October, 1355–1364.'
mla: Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit
Architectures.” IEEE International Conference on Computer Vision, vol.
2019–October, IEEE, 2019, pp. 1355–64, doi:10.1109/ICCV.2019.00144.
short: M. Phuong, C. Lampert, in:, IEEE International Conference on Computer Vision,
IEEE, 2019, pp. 1355–1364.
conference:
end_date: 2019-11-02
location: Seoul, Korea
name: 'ICCV: International Conference on Computer Vision'
start_date: 2019-10-27
date_created: 2020-02-11T09:06:57Z
date_published: 2019-10-01T00:00:00Z
date_updated: 2023-09-08T11:11:12Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1109/ICCV.2019.00144
ec_funded: 1
external_id:
isi:
- '000531438101047'
file:
- access_level: open_access
checksum: 7b77fb5c2d27c4c37a7612ba46a66117
content_type: application/pdf
creator: bphuong
date_created: 2020-02-11T09:06:39Z
date_updated: 2020-07-14T12:47:59Z
file_id: '7480'
file_name: main.pdf
file_size: 735768
relation: main_file
file_date_updated: 2020-07-14T12:47:59Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
page: 1355-1364
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: IEEE International Conference on Computer Vision
publication_identifier:
isbn:
- '9781728148038'
issn:
- '15505499'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '9418'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Distillation-based training for multi-exit architectures
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2019-October
year: '2019'
...
---
_id: '7640'
abstract:
- lang: eng
text: We propose a new model for detecting visual relationships, such as "person
riding motorcycle" or "bottle on table". This task is an important step towards
comprehensive structured mage understanding, going beyond detecting individual
objects. Our main novelty is a Box Attention mechanism that allows to model pairwise
interactions between objects using standard object detection pipelines. The resulting
model is conceptually clean, expressive and relies on well-justified training
and prediction procedures. Moreover, unlike previously proposed approaches, our
model does not introduce any additional complex components or hyperparameters
on top of those already required by the underlying detection model. We conduct
an experimental evaluation on two datasets, V-COCO and Open Images, demonstrating
strong quantitative and qualitative results.
article_number: 1749-1753
article_processing_charge: No
arxiv: 1
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Alina
full_name: Kuznetsova, Alina
last_name: Kuznetsova
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. Detecting visual relationships
using box attention. In: Proceedings of the 2019 International Conference on
Computer Vision Workshop. IEEE; 2019. doi:10.1109/ICCVW.2019.00217'
apa: 'Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting
visual relationships using box attention. In Proceedings of the 2019 International
Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217'
chicago: Kolesnikov, Alexander, Alina Kuznetsova, Christoph Lampert, and Vittorio
Ferrari. “Detecting Visual Relationships Using Box Attention.” In Proceedings
of the 2019 International Conference on Computer Vision Workshop. IEEE, 2019.
https://doi.org/10.1109/ICCVW.2019.00217.
ieee: A. Kolesnikov, A. Kuznetsova, C. Lampert, and V. Ferrari, “Detecting visual
relationships using box attention,” in Proceedings of the 2019 International
Conference on Computer Vision Workshop, Seoul, South Korea, 2019.
ista: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. 2019. Detecting visual
relationships using box attention. Proceedings of the 2019 International Conference
on Computer Vision Workshop. ICCVW: International Conference on Computer Vision
Workshop, 1749–1753.'
mla: Kolesnikov, Alexander, et al. “Detecting Visual Relationships Using Box Attention.”
Proceedings of the 2019 International Conference on Computer Vision Workshop,
1749–1753, IEEE, 2019, doi:10.1109/ICCVW.2019.00217.
short: A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of
the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
conference:
end_date: 2019-10-28
location: Seoul, South Korea
name: 'ICCVW: International Conference on Computer Vision Workshop'
start_date: 2019-10-27
date_created: 2020-04-05T22:00:51Z
date_published: 2019-10-01T00:00:00Z
date_updated: 2023-09-08T11:18:37Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCVW.2019.00217
ec_funded: 1
external_id:
arxiv:
- '1807.02136'
isi:
- '000554591601098'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1807.02136
month: '10'
oa: 1
oa_version: Preprint
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the 2019 International Conference on Computer Vision Workshop
publication_identifier:
isbn:
- '9781728150239'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Detecting visual relationships using box attention
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2019'
...
---
_id: '197'
abstract:
- lang: eng
text: Modern computer vision systems heavily rely on statistical machine learning
models, which typically require large amounts of labeled data to be learned reliably.
Moreover, very recently computer vision research widely adopted techniques for
representation learning, which further increase the demand for labeled data. However,
for many important practical problems there is relatively small amount of labeled
data available, so it is problematic to leverage full potential of the representation
learning methods. One way to overcome this obstacle is to invest substantial resources
into producing large labelled datasets. Unfortunately, this can be prohibitively
expensive in practice. In this thesis we focus on the alternative way of tackling
the aforementioned issue. We concentrate on methods, which make use of weakly-labeled
or even unlabeled data. Specifically, the first half of the thesis is dedicated
to the semantic image segmentation task. We develop a technique, which achieves
competitive segmentation performance and only requires annotations in a form of
global image-level labels instead of dense segmentation masks. Subsequently, we
present a new methodology, which further improves segmentation performance by
leveraging tiny additional feedback from a human annotator. By using our methods
practitioners can greatly reduce the amount of data annotation effort, which is
required to learn modern image segmentation models. In the second half of the
thesis we focus on methods for learning from unlabeled visual data. We study a
family of autoregressive models for modeling structure of natural images and discuss
potential applications of these models. Moreover, we conduct in-depth study of
one of these applications, where we develop the state-of-the-art model for the
probabilistic image colorization task.
acknowledgement: I also gratefully acknowledge the support of NVIDIA Corporation with
the donation of the GPUs used for this research.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
citation:
ama: Kolesnikov A. Weakly-Supervised Segmentation and Unsupervised Modeling of Natural
Images. 2018. doi:10.15479/AT:ISTA:th_1021
apa: Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_1021
chicago: Kolesnikov, Alexander. “Weakly-Supervised Segmentation and Unsupervised
Modeling of Natural Images.” Institute of Science and Technology Austria, 2018.
https://doi.org/10.15479/AT:ISTA:th_1021.
ieee: A. Kolesnikov, “Weakly-Supervised Segmentation and Unsupervised Modeling of
Natural Images,” Institute of Science and Technology Austria, 2018.
ista: Kolesnikov A. 2018. Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria.
mla: Kolesnikov, Alexander. Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th_1021.
short: A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of
Natural Images, Institute of Science and Technology Austria, 2018.
date_created: 2018-12-11T11:45:09Z
date_published: 2018-05-25T00:00:00Z
date_updated: 2023-09-07T12:51:46Z
day: '25'
ddc:
- '004'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:th_1021
ec_funded: 1
file:
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checksum: bc678e02468d8ebc39dc7267dfb0a1c4
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date_created: 2018-12-12T10:14:57Z
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has_accepted_license: '1'
language:
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month: '05'
oa: 1
oa_version: Published Version
page: '113'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '7718'
pubrep_id: '1021'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '6011'
abstract:
- lang: eng
text: 'We establish a data-dependent notion of algorithmic stability for Stochastic
Gradient Descent (SGD), and employ it to develop novel generalization bounds.
This is in contrast to previous distribution-free algorithmic stability results
for SGD which depend on the worst-case constants. By virtue of the data-dependent
argument, our bounds provide new insights into learning with SGD on convex and
non-convex problems. In the convex case, we show that the bound on the generalization
error depends on the risk at the initialization point. In the non-convex case,
we prove that the expected curvature of the objective function around the initialization
point has crucial influence on the generalization error. In both cases, our results
suggest a simple data-driven strategy to stabilize SGD by pre-screening its initialization.
As a corollary, our results allow us to show optimistic generalization bounds
that exhibit fast convergence rates for SGD subject to a vanishing empirical risk
and low noise of stochastic gradient. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Ilja
full_name: Kuzborskij, Ilja
last_name: Kuzborskij
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kuzborskij I, Lampert C. Data-dependent stability of stochastic gradient descent.
In: Proceedings of the 35 Th International Conference on Machine Learning.
Vol 80. ML Research Press; 2018:2815-2824.'
apa: 'Kuzborskij, I., & Lampert, C. (2018). Data-dependent stability of stochastic
gradient descent. In Proceedings of the 35 th International Conference on Machine
Learning (Vol. 80, pp. 2815–2824). Stockholm, Sweden: ML Research Press.'
chicago: Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic
Gradient Descent.” In Proceedings of the 35 Th International Conference on
Machine Learning, 80:2815–24. ML Research Press, 2018.
ieee: I. Kuzborskij and C. Lampert, “Data-dependent stability of stochastic gradient
descent,” in Proceedings of the 35 th International Conference on Machine Learning,
Stockholm, Sweden, 2018, vol. 80, pp. 2815–2824.
ista: 'Kuzborskij I, Lampert C. 2018. Data-dependent stability of stochastic gradient
descent. Proceedings of the 35 th International Conference on Machine Learning.
ICML: International Conference on Machine Learning vol. 80, 2815–2824.'
mla: Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic
Gradient Descent.” Proceedings of the 35 Th International Conference on Machine
Learning, vol. 80, ML Research Press, 2018, pp. 2815–24.
short: I. Kuzborskij, C. Lampert, in:, Proceedings of the 35 Th International Conference
on Machine Learning, ML Research Press, 2018, pp. 2815–2824.
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: 'ICML: International Conference on Machine Learning'
start_date: 2018-07-10
date_created: 2019-02-14T14:51:57Z
date_published: 2018-02-01T00:00:00Z
date_updated: 2023-10-17T09:51:13Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1703.01678'
isi:
- '000683379202095'
intvolume: ' 80'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1703.01678
month: '02'
oa: 1
oa_version: Preprint
page: 2815-2824
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the 35 th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Data-dependent stability of stochastic gradient descent
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 80
year: '2018'
...
---
_id: '68'
abstract:
- lang: eng
text: The most common assumption made in statistical learning theory is the assumption
of the independent and identically distributed (i.i.d.) data. While being very
convenient mathematically, it is often very clearly violated in practice. This
disparity between the machine learning theory and applications underlies a growing
demand in the development of algorithms that learn from dependent data and theory
that can provide generalization guarantees similar to the independent situations.
This thesis is dedicated to two variants of dependencies that can arise in practice.
One is a dependence on the level of samples in a single learning task. Another
dependency type arises in the multi-task setting when the tasks are dependent
on each other even though the data for them can be i.i.d. In both cases we model
the data (samples or tasks) as stochastic processes and introduce new algorithms
for both settings that take into account and exploit the resulting dependencies.
We prove the theoretical guarantees on the performance of the introduced algorithms
under different evaluation criteria and, in addition, we compliment the theoretical
study by the empirical one, where we evaluate some of the algorithms on two real
world datasets to highlight their practical applicability.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Alexander
full_name: Zimin, Alexander
id: 37099E9C-F248-11E8-B48F-1D18A9856A87
last_name: Zimin
citation:
ama: Zimin A. Learning from dependent data. 2018. doi:10.15479/AT:ISTA:TH1048
apa: Zimin, A. (2018). Learning from dependent data. Institute of Science
and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH1048
chicago: Zimin, Alexander. “Learning from Dependent Data.” Institute of Science
and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:TH1048.
ieee: A. Zimin, “Learning from dependent data,” Institute of Science and Technology
Austria, 2018.
ista: Zimin A. 2018. Learning from dependent data. Institute of Science and Technology
Austria.
mla: Zimin, Alexander. Learning from Dependent Data. Institute of Science
and Technology Austria, 2018, doi:10.15479/AT:ISTA:TH1048.
short: A. Zimin, Learning from Dependent Data, Institute of Science and Technology
Austria, 2018.
date_created: 2018-12-11T11:44:27Z
date_published: 2018-09-01T00:00:00Z
date_updated: 2023-09-07T12:29:07Z
day: '01'
ddc:
- '004'
- '519'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:TH1048
ec_funded: 1
file:
- access_level: open_access
checksum: e849dd40a915e4d6c5572b51b517f098
content_type: application/pdf
creator: dernst
date_created: 2019-04-09T07:32:47Z
date_updated: 2020-07-14T12:47:40Z
file_id: '6253'
file_name: 2018_Thesis_Zimin.pdf
file_size: 1036137
relation: main_file
- access_level: closed
checksum: da092153cec55c97461bd53c45c5d139
content_type: application/zip
creator: dernst
date_created: 2019-04-09T07:32:47Z
date_updated: 2020-07-14T12:47:40Z
file_id: '6254'
file_name: 2018_Thesis_Zimin_Source.zip
file_size: 637490
relation: source_file
file_date_updated: 2020-07-14T12:47:40Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '92'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '7986'
pubrep_id: '1048'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Learning from dependent data
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '1000'
abstract:
- lang: eng
text: 'We study probabilistic models of natural images and extend the autoregressive
family of PixelCNN models by incorporating latent variables. Subsequently, we
describe two new generative image models that exploit different image transformations
as latent variables: a quantized grayscale view of the image or a multi-resolution
image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN
models: 1) their tendency to focus on low-level image details, while largely ignoring
high-level image information, such as object shapes, and 2) their computationally
costly procedure for image sampling. We experimentally demonstrate benefits of
our LatentPixelCNN models, in particular showing that they produce much more realistically
looking image samples than previous state-of-the-art probabilistic models. '
acknowledgement: We thank Tim Salimans for spotting a mistake in our preliminary arXiv
manuscript. This work was funded by the European Research Council under the European
Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 308036.
article_processing_charge: No
arxiv: 1
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. PixelCNN models with auxiliary variables for natural
image modeling. In: 34th International Conference on Machine Learning.
Vol 70. JMLR; 2017:1905-1914.'
apa: 'Kolesnikov, A., & Lampert, C. (2017). PixelCNN models with auxiliary variables
for natural image modeling. In 34th International Conference on Machine Learning
(Vol. 70, pp. 1905–1914). Sydney, Australia: JMLR.'
chicago: Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary
Variables for Natural Image Modeling.” In 34th International Conference on
Machine Learning, 70:1905–14. JMLR, 2017.
ieee: A. Kolesnikov and C. Lampert, “PixelCNN models with auxiliary variables for
natural image modeling,” in 34th International Conference on Machine Learning,
Sydney, Australia, 2017, vol. 70, pp. 1905–1914.
ista: 'Kolesnikov A, Lampert C. 2017. PixelCNN models with auxiliary variables for
natural image modeling. 34th International Conference on Machine Learning. ICML:
International Conference on Machine Learning vol. 70, 1905–1914.'
mla: Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary
Variables for Natural Image Modeling.” 34th International Conference on Machine
Learning, vol. 70, JMLR, 2017, pp. 1905–14.
short: A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine
Learning, JMLR, 2017, pp. 1905–1914.
conference:
end_date: 2017-08-11
location: Sydney, Australia
name: 'ICML: International Conference on Machine Learning'
start_date: 2017-08-06
date_created: 2018-12-11T11:49:37Z
date_published: 2017-08-01T00:00:00Z
date_updated: 2023-09-22T09:50:41Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1612.08185'
isi:
- '000683309501102'
has_accepted_license: '1'
intvolume: ' 70'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1612.08185
month: '08'
oa: 1
oa_version: Submitted Version
page: 1905 - 1914
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: 34th International Conference on Machine Learning
publication_identifier:
isbn:
- 978-151085514-4
publication_status: published
publisher: JMLR
publist_id: '6398'
quality_controlled: '1'
scopus_import: '1'
status: public
title: PixelCNN models with auxiliary variables for natural image modeling
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 70
year: '2017'
...
---
_id: '1108'
abstract:
- lang: eng
text: In this work we study the learnability of stochastic processes with respect
to the conditional risk, i.e. the existence of a learning algorithm that improves
its next-step performance with the amount of observed data. We introduce a notion
of pairwise discrepancy between conditional distributions at different times steps
and show how certain properties of these discrepancies can be used to construct
a successful learning algorithm. Our main results are two theorems that establish
criteria for learnability for many classes of stochastic processes, including
all special cases studied previously in the literature.
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Alexander
full_name: Zimin, Alexander
id: 37099E9C-F248-11E8-B48F-1D18A9856A87
last_name: Zimin
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Zimin A, Lampert C. Learning theory for conditional risk minimization. In:
Vol 54. ML Research Press; 2017:213-222.'
apa: 'Zimin, A., & Lampert, C. (2017). Learning theory for conditional risk
minimization (Vol. 54, pp. 213–222). Presented at the AISTATS: Artificial Intelligence
and Statistics, Fort Lauderdale, FL, United States: ML Research Press.'
chicago: Zimin, Alexander, and Christoph Lampert. “Learning Theory for Conditional
Risk Minimization,” 54:213–22. ML Research Press, 2017.
ieee: 'A. Zimin and C. Lampert, “Learning theory for conditional risk minimization,”
presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale,
FL, United States, 2017, vol. 54, pp. 213–222.'
ista: 'Zimin A, Lampert C. 2017. Learning theory for conditional risk minimization.
AISTATS: Artificial Intelligence and Statistics, PMLR, vol. 54, 213–222.'
mla: Zimin, Alexander, and Christoph Lampert. Learning Theory for Conditional
Risk Minimization. Vol. 54, ML Research Press, 2017, pp. 213–22.
short: A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222.
conference:
end_date: 2017-04-22
location: Fort Lauderdale, FL, United States
name: 'AISTATS: Artificial Intelligence and Statistics'
start_date: 2017-04-20
date_created: 2018-12-11T11:50:11Z
date_published: 2017-04-01T00:00:00Z
date_updated: 2023-10-17T10:01:12Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
isi:
- '000509368500024'
intvolume: ' 54'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://proceedings.mlr.press/v54/zimin17a/zimin17a.pdf
month: '04'
oa: 1
oa_version: Submitted Version
page: 213 - 222
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: ML Research Press
publist_id: '6261'
quality_controlled: '1'
status: public
title: Learning theory for conditional risk minimization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 54
year: '2017'
...
---
_id: '6841'
abstract:
- lang: eng
text: In classical machine learning, regression is treated as a black box process
of identifying a suitable function from a hypothesis set without attempting to
gain insight into the mechanism connecting inputs and outputs. In the natural
sciences, however, finding an interpretable function for a phenomenon is the prime
goal as it allows to understand and generalize results. This paper proposes a
novel type of function learning network, called equation learner (EQL), that can
learn analytical expressions and is able to extrapolate to unseen domains. It
is implemented as an end-to-end differentiable feed-forward network and allows
for efficient gradient based training. Due to sparsity regularization concise
interpretable expressions can be obtained. Often the true underlying source expression
is identified.
arxiv: 1
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Martius GS, Lampert C. Extrapolation and learning equations. In: 5th International
Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
International Conference on Learning Representations; 2017.'
apa: 'Martius, G. S., & Lampert, C. (2017). Extrapolation and learning equations.
In 5th International Conference on Learning Representations, ICLR 2017 - Workshop
Track Proceedings. Toulon, France: International Conference on Learning Representations.'
chicago: Martius, Georg S, and Christoph Lampert. “Extrapolation and Learning Equations.”
In 5th International Conference on Learning Representations, ICLR 2017 - Workshop
Track Proceedings. International Conference on Learning Representations, 2017.
ieee: G. S. Martius and C. Lampert, “Extrapolation and learning equations,” in 5th
International Conference on Learning Representations, ICLR 2017 - Workshop Track
Proceedings, Toulon, France, 2017.
ista: 'Martius GS, Lampert C. 2017. Extrapolation and learning equations. 5th International
Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
ICLR: International Conference on Learning Representations.'
mla: Martius, Georg S., and Christoph Lampert. “Extrapolation and Learning Equations.”
5th International Conference on Learning Representations, ICLR 2017 - Workshop
Track Proceedings, International Conference on Learning Representations, 2017.
short: G.S. Martius, C. Lampert, in:, 5th International Conference on Learning Representations,
ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations,
2017.
conference:
end_date: 2017-04-26
location: Toulon, France
name: 'ICLR: International Conference on Learning Representations'
start_date: 2017-04-24
date_created: 2019-09-01T22:01:00Z
date_published: 2017-02-21T00:00:00Z
date_updated: 2021-01-12T08:09:17Z
day: '21'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1610.02995'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1610.02995
month: '02'
oa: 1
oa_version: Preprint
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: 5th International Conference on Learning Representations, ICLR 2017 -
Workshop Track Proceedings
publication_status: published
publisher: International Conference on Learning Representations
quality_controlled: '1'
scopus_import: 1
status: public
title: Extrapolation and learning equations
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '911'
abstract:
- lang: eng
text: We develop a probabilistic technique for colorizing grayscale natural images.
In light of the intrinsic uncertainty of this task, the proposed probabilistic
framework has numerous desirable properties. In particular, our model is able
to produce multiple plausible and vivid colorizations for a given grayscale image
and is one of the first colorization models to provide a proper stochastic sampling
scheme. Moreover, our training procedure is supported by a rigorous theoretical
framework that does not require any ad hoc heuristics and allows for efficient
modeling and learning of the joint pixel color distribution.We demonstrate strong
quantitative and qualitative experimental results on the CIFAR-10 dataset and
the challenging ILSVRC 2012 dataset.
article_processing_charge: No
arxiv: 1
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Kolesnikov A, Lampert C. Probabilistic image colorization. In: BMVA
Press; 2017:85.1-85.12. doi:10.5244/c.31.85'
apa: 'Royer, A., Kolesnikov, A., & Lampert, C. (2017). Probabilistic image colorization
(p. 85.1-85.12). Presented at the BMVC: British Machine Vision Conference, London,
United Kingdom: BMVA Press. https://doi.org/10.5244/c.31.85'
chicago: Royer, Amélie, Alexander Kolesnikov, and Christoph Lampert. “Probabilistic
Image Colorization,” 85.1-85.12. BMVA Press, 2017. https://doi.org/10.5244/c.31.85.
ieee: 'A. Royer, A. Kolesnikov, and C. Lampert, “Probabilistic image colorization,”
presented at the BMVC: British Machine Vision Conference, London, United Kingdom,
2017, p. 85.1-85.12.'
ista: 'Royer A, Kolesnikov A, Lampert C. 2017. Probabilistic image colorization.
BMVC: British Machine Vision Conference, 85.1-85.12.'
mla: Royer, Amélie, et al. Probabilistic Image Colorization. BMVA Press,
2017, p. 85.1-85.12, doi:10.5244/c.31.85.
short: A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, 2017, p. 85.1-85.12.
conference:
end_date: 2017-09-07
location: London, United Kingdom
name: 'BMVC: British Machine Vision Conference'
start_date: 2017-09-04
date_created: 2018-12-11T11:49:09Z
date_published: 2017-09-01T00:00:00Z
date_updated: 2023-10-16T10:04:02Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.5244/c.31.85
ec_funded: 1
external_id:
arxiv:
- '1705.04258'
file:
- access_level: open_access
content_type: application/pdf
creator: dernst
date_created: 2020-08-10T07:14:33Z
date_updated: 2020-08-10T07:14:33Z
file_id: '8224'
file_name: 2017_BMVC_Royer.pdf
file_size: 1625363
relation: main_file
success: 1
file_date_updated: 2020-08-10T07:14:33Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 85.1-85.12
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
eisbn:
- 190172560X
publication_status: published
publisher: BMVA Press
publist_id: '6532'
quality_controlled: '1'
related_material:
record:
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Probabilistic image colorization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '998'
abstract:
- lang: eng
text: 'A major open problem on the road to artificial intelligence is the development
of incrementally learning systems that learn about more and more concepts over
time from a stream of data. In this work, we introduce a new training strategy,
iCaRL, that allows learning in such a class-incremental way: only the training
data for a small number of classes has to be present at the same time and new
classes can be added progressively. iCaRL learns strong classifiers and a data
representation simultaneously. This distinguishes it from earlier works that were
fundamentally limited to fixed data representations and therefore incompatible
with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet
ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period
of time where other strategies quickly fail. '
article_processing_charge: No
author:
- first_name: Sylvestre Alvise
full_name: Rebuffi, Sylvestre Alvise
last_name: Rebuffi
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. iCaRL: Incremental classifier
and representation learning. In: Vol 2017. IEEE; 2017:5533-5542. doi:10.1109/CVPR.2017.587'
apa: 'Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. (2017). iCaRL:
Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542).
Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA,
United States: IEEE. https://doi.org/10.1109/CVPR.2017.587'
chicago: 'Rebuffi, Sylvestre Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph
Lampert. “ICaRL: Incremental Classifier and Representation Learning,” 2017:5533–42.
IEEE, 2017. https://doi.org/10.1109/CVPR.2017.587.'
ieee: 'S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert, “iCaRL: Incremental
classifier and representation learning,” presented at the CVPR: Computer Vision
and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 5533–5542.'
ista: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. 2017. iCaRL: Incremental classifier
and representation learning. CVPR: Computer Vision and Pattern Recognition vol.
2017, 5533–5542.'
mla: 'Rebuffi, Sylvestre Alvise, et al. ICaRL: Incremental Classifier and Representation
Learning. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:10.1109/CVPR.2017.587.'
short: S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
conference:
end_date: 2017-07-26
location: Honolulu, HA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2017-07-21
date_created: 2018-12-11T11:49:37Z
date_published: 2017-04-14T00:00:00Z
date_updated: 2023-09-22T09:51:58Z
day: '14'
department:
- _id: ChLa
- _id: ChWo
doi: 10.1109/CVPR.2017.587
ec_funded: 1
external_id:
isi:
- '000418371405066'
intvolume: ' 2017'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1611.07725
month: '04'
oa: 1
oa_version: Submitted Version
page: 5533 - 5542
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
isbn:
- 978-153860457-1
publication_status: published
publisher: IEEE
publist_id: '6400'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'iCaRL: Incremental classifier and representation learning'
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2017
year: '2017'
...
---
_id: '999'
abstract:
- lang: eng
text: 'In multi-task learning, a learner is given a collection of prediction tasks
and needs to solve all of them. In contrast to previous work, which required that
annotated training data must be available for all tasks, we consider a new setting,
in which for some tasks, potentially most of them, only unlabeled training data
is provided. Consequently, to solve all tasks, information must be transferred
between tasks with labels and tasks without labels. Focusing on an instance-based
transfer method we analyze two variants of this setting: when the set of labeled
tasks is fixed, and when it can be actively selected by the learner. We state
and prove a generalization bound that covers both scenarios and derive from it
an algorithm for making the choice of labeled tasks (in the active case) and for
transferring information between the tasks in a principled way. We also illustrate
the effectiveness of the algorithm on synthetic and real data. '
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Lampert C. Multi-task learning with labeled and unlabeled tasks.
In: Vol 70. ML Research Press; 2017:2807-2816.'
apa: 'Pentina, A., & Lampert, C. (2017). Multi-task learning with labeled and
unlabeled tasks (Vol. 70, pp. 2807–2816). Presented at the ICML: International
Conference on Machine Learning, Sydney, Australia: ML Research Press.'
chicago: Pentina, Anastasia, and Christoph Lampert. “Multi-Task Learning with Labeled
and Unlabeled Tasks,” 70:2807–16. ML Research Press, 2017.
ieee: 'A. Pentina and C. Lampert, “Multi-task learning with labeled and unlabeled
tasks,” presented at the ICML: International Conference on Machine Learning, Sydney,
Australia, 2017, vol. 70, pp. 2807–2816.'
ista: 'Pentina A, Lampert C. 2017. Multi-task learning with labeled and unlabeled
tasks. ICML: International Conference on Machine Learning, PMLR, vol. 70, 2807–2816.'
mla: Pentina, Anastasia, and Christoph Lampert. Multi-Task Learning with Labeled
and Unlabeled Tasks. Vol. 70, ML Research Press, 2017, pp. 2807–16.
short: A. Pentina, C. Lampert, in:, ML Research Press, 2017, pp. 2807–2816.
conference:
end_date: 2017-08-11
location: Sydney, Australia
name: 'ICML: International Conference on Machine Learning'
start_date: 2017-08-06
date_created: 2018-12-11T11:49:37Z
date_published: 2017-06-08T00:00:00Z
date_updated: 2023-10-17T11:53:32Z
day: '08'
department:
- _id: ChLa
ec_funded: 1
external_id:
isi:
- '000683309502093'
intvolume: ' 70'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1602.06518
month: '06'
oa: 1
oa_version: Submitted Version
page: 2807 - 2816
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
isbn:
- '9781510855144'
publication_status: published
publisher: ML Research Press
publist_id: '6399'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Multi-task learning with labeled and unlabeled tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 70
year: '2017'
...
---
_id: '1098'
abstract:
- lang: eng
text: Better understanding of the potential benefits of information transfer and
representation learning is an important step towards the goal of building intelligent
systems that are able to persist in the world and learn over time. In this work,
we consider a setting where the learner encounters a stream of tasks but is able
to retain only limited information from each encountered task, such as a learned
predictor. In contrast to most previous works analyzing this scenario, we do not
make any distributional assumptions on the task generating process. Instead, we
formulate a complexity measure that captures the diversity of the observed tasks.
We provide a lifelong learning algorithm with error guarantees for every observed
task (rather than on average). We show sample complexity reductions in comparison
to solving every task in isolation in terms of our task complexity measure. Further,
our algorithmic framework can naturally be viewed as learning a representation
from encountered tasks with a neural network.
acknowledgement: "This work was in parts funded by the European Research Council under
the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement
no 308036.\r\n\r\n"
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Ruth
full_name: Urner, Ruth
last_name: Urner
citation:
ama: 'Pentina A, Urner R. Lifelong learning with weighted majority votes. In: Vol
29. Neural Information Processing Systems; 2016:3619-3627.'
apa: 'Pentina, A., & Urner, R. (2016). Lifelong learning with weighted majority
votes (Vol. 29, pp. 3619–3627). Presented at the NIPS: Neural Information Processing
Systems, Barcelona, Spain: Neural Information Processing Systems.'
chicago: Pentina, Anastasia, and Ruth Urner. “Lifelong Learning with Weighted Majority
Votes,” 29:3619–27. Neural Information Processing Systems, 2016.
ieee: 'A. Pentina and R. Urner, “Lifelong learning with weighted majority votes,”
presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain,
2016, vol. 29, pp. 3619–3627.'
ista: 'Pentina A, Urner R. 2016. Lifelong learning with weighted majority votes.
NIPS: Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 29, 3619–3627.'
mla: Pentina, Anastasia, and Ruth Urner. Lifelong Learning with Weighted Majority
Votes. Vol. 29, Neural Information Processing Systems, 2016, pp. 3619–27.
short: A. Pentina, R. Urner, in:, Neural Information Processing Systems, 2016, pp.
3619–3627.
conference:
end_date: 2016-12-10
location: Barcelona, Spain
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:50:08Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2021-01-12T06:48:15Z
day: '01'
ddc:
- '006'
department:
- _id: ChLa
ec_funded: 1
file:
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:42Z
date_updated: 2018-12-12T10:12:42Z
file_id: '4961'
file_name: IST-2017-775-v1+1_main.pdf
file_size: 237111
relation: main_file
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:43Z
date_updated: 2018-12-12T10:12:43Z
file_id: '4962'
file_name: IST-2017-775-v1+2_supplementary.pdf
file_size: 185818
relation: main_file
file_date_updated: 2018-12-12T10:12:43Z
has_accepted_license: '1'
intvolume: ' 29'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 3619-3627
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '6277'
pubrep_id: '775'
quality_controlled: '1'
scopus_import: 1
status: public
title: Lifelong learning with weighted majority votes
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '1102'
abstract:
- lang: eng
text: Weakly-supervised object localization methods tend to fail for object classes
that consistently co-occur with the same background elements, e.g. trains on tracks.
We propose a method to overcome these failures by adding a very small amount of
model-specific additional annotation. The main idea is to cluster a deep network\'s
mid-level representations and assign object or distractor labels to each cluster.
Experiments show substantially improved localization results on the challenging
ILSVC2014 dataset for bounding box detection and the PASCAL VOC2012 dataset for
semantic segmentation.
acknowledgement: "This work was funded in parts by the European Research Council\r\nunder
the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant\r\nagreement
no 308036. We gratefully acknowledge the support of NVIDIA Corporation with\r\nthe
donation of the GPUs used for this research."
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. Improving weakly-supervised object localization by
micro-annotation. In: Proceedings of the British Machine Vision Conference
2016. Vol 2016-September. BMVA Press; 2016:92.1-92.12. doi:10.5244/C.30.92'
apa: 'Kolesnikov, A., & Lampert, C. (2016). Improving weakly-supervised object
localization by micro-annotation. In Proceedings of the British Machine Vision
Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom:
BMVA Press. https://doi.org/10.5244/C.30.92'
chicago: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised
Object Localization by Micro-Annotation.” In Proceedings of the British Machine
Vision Conference 2016, 2016–September:92.1-92.12. BMVA Press, 2016. https://doi.org/10.5244/C.30.92.
ieee: A. Kolesnikov and C. Lampert, “Improving weakly-supervised object localization
by micro-annotation,” in Proceedings of the British Machine Vision Conference
2016, York, United Kingdom, 2016, vol. 2016–September, p. 92.1-92.12.
ista: 'Kolesnikov A, Lampert C. 2016. Improving weakly-supervised object localization
by micro-annotation. Proceedings of the British Machine Vision Conference 2016.
BMVC: British Machine Vision Conference vol. 2016–September, 92.1-92.12.'
mla: Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised
Object Localization by Micro-Annotation.” Proceedings of the British Machine
Vision Conference 2016, vol. 2016–September, BMVA Press, 2016, p. 92.1-92.12,
doi:10.5244/C.30.92.
short: A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision
Conference 2016, BMVA Press, 2016, p. 92.1-92.12.
conference:
end_date: 2016-09-22
location: York, United Kingdom
name: 'BMVC: British Machine Vision Conference'
start_date: 2016-09-19
date_created: 2018-12-11T11:50:09Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2021-01-12T06:48:18Z
day: '01'
department:
- _id: ChLa
doi: 10.5244/C.30.92
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.bmva.org/bmvc/2016/papers/paper092/paper092.pdf
month: '09'
oa: 1
oa_version: Published Version
page: 92.1-92.12
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the British Machine Vision Conference 2016
publication_status: published
publisher: BMVA Press
publist_id: '6273'
quality_controlled: '1'
scopus_import: 1
status: public
title: Improving weakly-supervised object localization by micro-annotation
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016-September
year: '2016'
...
---
_id: '1126'
abstract:
- lang: eng
text: "Traditionally machine learning has been focusing on the problem of solving
a single\r\ntask in isolation. While being quite well understood, this approach
disregards an\r\nimportant aspect of human learning: when facing a new problem,
humans are able to\r\nexploit knowledge acquired from previously learned tasks.
Intuitively, access to several\r\nproblems simultaneously or sequentially could
also be advantageous for a machine\r\nlearning system, especially if these tasks
are closely related. Indeed, results of many\r\nempirical studies have provided
justification for this intuition. However, theoretical\r\njustifications of this
idea are rather limited.\r\nThe focus of this thesis is to expand the understanding
of potential benefits of information\r\ntransfer between several related learning
problems. We provide theoretical\r\nanalysis for three scenarios of multi-task
learning - multiple kernel learning, sequential\r\nlearning and active task selection.
We also provide a PAC-Bayesian perspective on\r\nlifelong learning and investigate
how the task generation process influences the generalization\r\nguarantees in
this scenario. In addition, we show how some of the obtained\r\ntheoretical results
can be used to derive principled multi-task and lifelong learning\r\nalgorithms
and illustrate their performance on various synthetic and real-world datasets."
acknowledgement: "First and foremost I would like to express my gratitude to my supervisor,
Christoph\r\nLampert. Thank you for your patience in teaching me all aspects of
doing research\r\n(including English grammar), for your trust in my capabilities
and endless support. Thank\r\nyou for granting me freedom in my research and, at
the same time, having time and\r\nhelping me cope with the consequences whenever
I needed it. Thank you for creating\r\nan excellent atmosphere in the group, it
was a great pleasure and honor to be a part of\r\nit. There could not have been
a better and more inspiring adviser and mentor.\r\nI thank Shai Ben-David for welcoming
me into his group at the University of Waterloo,\r\nfor inspiring discussions and
support. It was a great pleasure to work together. I am\r\nalso thankful to Ruth
Urner for hosting me at the Max-Planck Institute Tübingen, for the\r\nfruitful
collaboration and for taking care of me during that not-so-sunny month of May.\r\nI
thank Jan Maas for kindly joining my thesis committee despite the short notice and\r\nproviding
me with insightful comments.\r\nI would like to thank my colleagues for their support,
entertaining conversations and\r\nendless table soccer games we shared together:
Georg, Jan, Amelie and Emilie, Michal\r\nand Alex, Alex K. and Alex Z., Thomas,
Sameh, Vlad, Mayu, Nathaniel, Silvester, Neel,\r\nCsaba, Vladimir, Morten. Thank
you, Mabel and Ram, for the wonderful time we spent\r\ntogether. I am thankful to
Shrinu and Samira for taking care of me during my stay at the\r\nUniversity of Waterloo.
Special thanks to Viktoriia for her never-ending optimism and for\r\nbeing so inspiring
and supportive, especially at the beginning of my PhD journey.\r\nThanks to IST
administration, in particular, Vlad and Elisabeth for shielding me from\r\nmost
of the bureaucratic paperwork.\r\n\r\nThis dissertation would not have been possible
without funding from the European\r\nResearch Council under the European Union's
Seventh Framework Programme\r\n(FP7/2007-2013)/ERC grant agreement no 308036."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
citation:
ama: Pentina A. Theoretical foundations of multi-task lifelong learning. 2016. doi:10.15479/AT:ISTA:TH_776
apa: Pentina, A. (2016). Theoretical foundations of multi-task lifelong learning.
Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH_776
chicago: Pentina, Anastasia. “Theoretical Foundations of Multi-Task Lifelong Learning.”
Institute of Science and Technology Austria, 2016. https://doi.org/10.15479/AT:ISTA:TH_776.
ieee: A. Pentina, “Theoretical foundations of multi-task lifelong learning,” Institute
of Science and Technology Austria, 2016.
ista: Pentina A. 2016. Theoretical foundations of multi-task lifelong learning.
Institute of Science and Technology Austria.
mla: Pentina, Anastasia. Theoretical Foundations of Multi-Task Lifelong Learning.
Institute of Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:TH_776.
short: A. Pentina, Theoretical Foundations of Multi-Task Lifelong Learning, Institute
of Science and Technology Austria, 2016.
date_created: 2018-12-11T11:50:17Z
date_published: 2016-11-01T00:00:00Z
date_updated: 2023-09-07T11:52:03Z
day: '01'
ddc:
- '006'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:TH_776
ec_funded: 1
file:
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:07Z
date_updated: 2018-12-12T10:14:07Z
file_id: '5056'
file_name: IST-2017-776-v1+1_Pentina_Thesis_2016.pdf
file_size: 2140062
relation: main_file
file_date_updated: 2018-12-12T10:14:07Z
has_accepted_license: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: '127'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '6234'
pubrep_id: '776'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Theoretical foundations of multi-task lifelong learning
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2016'
...
---
_id: '1369'
abstract:
- lang: eng
text: 'We introduce a new loss function for the weakly-supervised training of semantic
image segmentation models based on three guiding principles: to seed with weak
localization cues, to expand objects based on the information about which classes
can occur in an image, and to constrain the segmentations to coincide with object
boundaries. We show experimentally that training a deep convolutional neural network
using the proposed loss function leads to substantially better segmentations than
previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset.
We furthermore give insight into the working mechanism of our method by a detailed
experimental study that illustrates how the segmentation quality is affected by
each term of the proposed loss function as well as their combinations.'
alternative_title:
- LNCS
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Lampert C. Seed, expand and constrain: Three principles for
weakly-supervised image segmentation. In: Vol 9908. Springer; 2016:695-711. doi:10.1007/978-3-319-46493-0_42'
apa: 'Kolesnikov, A., & Lampert, C. (2016). Seed, expand and constrain: Three
principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711).
Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The
Netherlands: Springer. https://doi.org/10.1007/978-3-319-46493-0_42'
chicago: 'Kolesnikov, Alexander, and Christoph Lampert. “Seed, Expand and Constrain:
Three Principles for Weakly-Supervised Image Segmentation,” 9908:695–711. Springer,
2016. https://doi.org/10.1007/978-3-319-46493-0_42.'
ieee: 'A. Kolesnikov and C. Lampert, “Seed, expand and constrain: Three principles
for weakly-supervised image segmentation,” presented at the ECCV: European Conference
on Computer Vision, Amsterdam, The Netherlands, 2016, vol. 9908, pp. 695–711.'
ista: 'Kolesnikov A, Lampert C. 2016. Seed, expand and constrain: Three principles
for weakly-supervised image segmentation. ECCV: European Conference on Computer
Vision, LNCS, vol. 9908, 695–711.'
mla: 'Kolesnikov, Alexander, and Christoph Lampert. Seed, Expand and Constrain:
Three Principles for Weakly-Supervised Image Segmentation. Vol. 9908, Springer,
2016, pp. 695–711, doi:10.1007/978-3-319-46493-0_42.'
short: A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
conference:
end_date: 2016-10-14
location: Amsterdam, The Netherlands
name: 'ECCV: European Conference on Computer Vision'
start_date: 2016-10-11
date_created: 2018-12-11T11:51:37Z
date_published: 2016-09-15T00:00:00Z
date_updated: 2021-01-12T06:50:12Z
day: '15'
department:
- _id: ChLa
doi: 10.1007/978-3-319-46493-0_42
ec_funded: 1
intvolume: ' 9908'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1603.06098
month: '09'
oa: 1
oa_version: Preprint
page: 695 - 711
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Springer
publist_id: '5842'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Seed, expand and constrain: Three principles for weakly-supervised image segmentation'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 9908
year: '2016'
...
---
_id: '1425'
abstract:
- lang: eng
text: 'In this work we aim at extending the theoretical foundations of lifelong
learning. Previous work analyzing this scenario is based on the assumption that
learning tasks are sampled i.i.d. from a task environment or limited to strongly
constrained data distributions. Instead, we study two scenarios when lifelong
learning is possible, even though the observed tasks do not form an i.i.d. sample:
first, when they are sampled from the same environment, but possibly with dependencies,
and second, when the task environment is allowed to change over time in a consistent
way. In the first case we prove a PAC-Bayesian theorem that can be seen as a direct
generalization of the analogous previous result for the i.i.d. case. For the second
scenario we propose to learn an inductive bias in form of a transfer procedure.
We present a generalization bound and show on a toy example how it can be used
to identify a beneficial transfer algorithm.'
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Lampert C. Lifelong learning with non-i.i.d. tasks. In: Vol 2015.
Neural Information Processing Systems; 2015:1540-1548.'
apa: 'Pentina, A., & Lampert, C. (2015). Lifelong learning with non-i.i.d. tasks
(Vol. 2015, pp. 1540–1548). Presented at the NIPS: Neural Information Processing
Systems, Montreal, Canada: Neural Information Processing Systems.'
chicago: Pentina, Anastasia, and Christoph Lampert. “Lifelong Learning with Non-i.i.d.
Tasks,” 2015:1540–48. Neural Information Processing Systems, 2015.
ieee: 'A. Pentina and C. Lampert, “Lifelong learning with non-i.i.d. tasks,” presented
at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2015, vol.
2015, pp. 1540–1548.'
ista: 'Pentina A, Lampert C. 2015. Lifelong learning with non-i.i.d. tasks. NIPS:
Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 2015, 1540–1548.'
mla: Pentina, Anastasia, and Christoph Lampert. Lifelong Learning with Non-i.i.d.
Tasks. Vol. 2015, Neural Information Processing Systems, 2015, pp. 1540–48.
short: A. Pentina, C. Lampert, in:, Neural Information Processing Systems, 2015,
pp. 1540–1548.
conference:
end_date: 2015-12-12
location: Montreal, Canada
name: 'NIPS: Neural Information Processing Systems'
start_date: 2015-12-07
date_created: 2018-12-11T11:51:57Z
date_published: 2015-01-01T00:00:00Z
date_updated: 2021-01-12T06:50:39Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
intvolume: ' 2015'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://papers.nips.cc/paper/6007-lifelong-learning-with-non-iid-tasks
month: '01'
oa: 1
oa_version: None
page: 1540 - 1548
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '5781'
quality_controlled: '1'
scopus_import: 1
status: public
title: Lifelong learning with non-i.i.d. tasks
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2015
year: '2015'
...
---
_id: '1706'
abstract:
- lang: eng
text: We consider a problem of learning kernels for use in SVM classification in
the multi-task and lifelong scenarios and provide generalization bounds on the
error of a large margin classifier. Our results show that, under mild conditions
on the family of kernels used for learning, solving several related tasks simultaneously
is beneficial over single task learning. In particular, as the number of observed
tasks grows, assuming that in the considered family of kernels there exists one
that yields low approximation error on all tasks, the overhead associated with
learning such a kernel vanishes and the complexity converges to that of learning
when this good kernel is given to the learner.
alternative_title:
- LNCS
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Shai
full_name: Ben David, Shai
last_name: Ben David
citation:
ama: 'Pentina A, Ben David S. Multi-task and lifelong learning of kernels. In: Vol
9355. Springer; 2015:194-208. doi:10.1007/978-3-319-24486-0_13'
apa: 'Pentina, A., & Ben David, S. (2015). Multi-task and lifelong learning
of kernels (Vol. 9355, pp. 194–208). Presented at the ALT: Algorithmic Learning
Theory, Banff, AB, Canada: Springer. https://doi.org/10.1007/978-3-319-24486-0_13'
chicago: Pentina, Anastasia, and Shai Ben David. “Multi-Task and Lifelong Learning
of Kernels,” 9355:194–208. Springer, 2015. https://doi.org/10.1007/978-3-319-24486-0_13.
ieee: 'A. Pentina and S. Ben David, “Multi-task and lifelong learning of kernels,”
presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada, 2015, vol.
9355, pp. 194–208.'
ista: 'Pentina A, Ben David S. 2015. Multi-task and lifelong learning of kernels.
ALT: Algorithmic Learning Theory, LNCS, vol. 9355, 194–208.'
mla: Pentina, Anastasia, and Shai Ben David. Multi-Task and Lifelong Learning
of Kernels. Vol. 9355, Springer, 2015, pp. 194–208, doi:10.1007/978-3-319-24486-0_13.
short: A. Pentina, S. Ben David, in:, Springer, 2015, pp. 194–208.
conference:
end_date: 2015-10-06
location: Banff, AB, Canada
name: 'ALT: Algorithmic Learning Theory'
start_date: 2015-10-04
date_created: 2018-12-11T11:53:35Z
date_published: 2015-01-01T00:00:00Z
date_updated: 2021-01-12T06:52:39Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-3-319-24486-0_13
ec_funded: 1
intvolume: ' 9355'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1602.06531
month: '01'
oa: 1
oa_version: Preprint
page: 194 - 208
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: Springer
publist_id: '5430'
quality_controlled: '1'
scopus_import: 1
status: public
title: Multi-task and lifelong learning of kernels
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9355
year: '2015'
...