---
_id: '1857'
abstract:
- lang: eng
  text: 'Sharing information between multiple tasks enables algorithms to achieve
    good generalization performance even from small amounts of training data. However,
    in a realistic scenario of multi-task learning not all tasks are equally related
    to each other, hence it could be advantageous to transfer information only between
    the most related tasks. In this work we propose an approach that processes multiple
    tasks in a sequence with sharing between subsequent tasks instead of solving all
    tasks jointly. Subsequently, we address the question of curriculum learning of
    tasks, i.e. finding the best order of tasks to be learned. Our approach is based
    on a generalization bound criterion for choosing the task order that optimizes
    the average expected classification performance over all tasks. Our experimental
    results show that learning multiple related tasks sequentially can be more effective
    than learning them jointly, the order in which tasks are being solved affects
    the overall performance, and that our model is able to automatically discover
    the favourable order of tasks. '
author:
- first_name: Anastasia
  full_name: Pentina, Anastasia
  id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
  last_name: Pentina
- first_name: Viktoriia
  full_name: Sharmanska, Viktoriia
  id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
  last_name: Sharmanska
  orcid: 0000-0003-0192-9308
- 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, Sharmanska V, Lampert C. Curriculum learning of multiple tasks.
    In: IEEE; 2015:5492-5500. doi:<a href="https://doi.org/10.1109/CVPR.2015.7299188">10.1109/CVPR.2015.7299188</a>'
  apa: 'Pentina, A., Sharmanska, V., &#38; Lampert, C. (2015). Curriculum learning
    of multiple tasks (pp. 5492–5500). Presented at the CVPR: Computer Vision and
    Pattern Recognition, Boston, MA, United States: IEEE. <a href="https://doi.org/10.1109/CVPR.2015.7299188">https://doi.org/10.1109/CVPR.2015.7299188</a>'
  chicago: Pentina, Anastasia, Viktoriia Sharmanska, and Christoph Lampert. “Curriculum
    Learning of Multiple Tasks,” 5492–5500. IEEE, 2015. <a href="https://doi.org/10.1109/CVPR.2015.7299188">https://doi.org/10.1109/CVPR.2015.7299188</a>.
  ieee: 'A. Pentina, V. Sharmanska, and C. Lampert, “Curriculum learning of multiple
    tasks,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston,
    MA, United States, 2015, pp. 5492–5500.'
  ista: 'Pentina A, Sharmanska V, Lampert C. 2015. Curriculum learning of multiple
    tasks. CVPR: Computer Vision and Pattern Recognition, 5492–5500.'
  mla: Pentina, Anastasia, et al. <i>Curriculum Learning of Multiple Tasks</i>. IEEE,
    2015, pp. 5492–500, doi:<a href="https://doi.org/10.1109/CVPR.2015.7299188">10.1109/CVPR.2015.7299188</a>.
  short: A. Pentina, V. Sharmanska, C. Lampert, in:, IEEE, 2015, pp. 5492–5500.
conference:
  end_date: 2015-06-12
  location: Boston, MA, United States
  name: 'CVPR: Computer Vision and Pattern Recognition'
  start_date: 2015-06-07
date_created: 2018-12-11T11:54:23Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2023-02-23T10:17:31Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/CVPR.2015.7299188
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://arxiv.org/abs/1412.1353
month: '06'
oa: 1
oa_version: Preprint
page: 5492 - 5500
publication_status: published
publisher: IEEE
publist_id: '5243'
quality_controlled: '1'
scopus_import: 1
status: public
title: Curriculum learning of multiple tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1401'
abstract:
- lang: eng
  text: 'The human ability to recognize objects in complex scenes has driven research
    in the computer vision field over couple of decades. This thesis focuses on the
    object recognition task in images. That is, given the image, we want the computer
    system to be able to predict the class of the object that appears in the image.
    A recent successful attempt to bridge semantic understanding of the image perceived
    by humans and by computers uses attribute-based models. Attributes are semantic
    properties of the objects shared across different categories, which humans and
    computers can decide on. To explore the attribute-based models we take a statistical
    machine learning approach, and address two key learning challenges in view of
    object recognition task: learning augmented attributes as mid-level discriminative
    feature representation, and learning with attributes as privileged information.
    Our main contributions are parametric and non-parametric models and algorithms
    to solve these frameworks. In the parametric approach, we explore an autoencoder
    model combined with the large margin nearest neighbor principle for mid-level
    feature learning, and linear support vector machines for learning with privileged
    information. In the non-parametric approach, we propose a supervised Indian Buffet
    Process for automatic augmentation of semantic attributes, and explore the Gaussian
    Processes classification framework for learning with privileged information. A
    thorough experimental analysis shows the effectiveness of the proposed models
    in both parametric and non-parametric views.'
acknowledgement: "I would like to thank my supervisor, Christoph Lampert, for guidance
  throughout my studies and for patience in transforming me into a scientist, and
  my thesis committee, Chris Wojtan and Horst Bischof, for their help and advice.
  \r\n\r\nI would like to thank Elisabeth Hacker who perfectly assisted all my administrative
  needs and was always nice and friendly to me, and the campus team for making the
  IST Austria campus my second home. \r\nI was honored to collaborate with brilliant
  researchers and to learn from their experience. Undoubtedly, I learned most of all
  from Novi Quadrianto: brainstorming our projects and getting exciting results was
  the most enjoyable part of my work – thank you! I am also grateful to David Knowles,
  Zoubin Ghahramani, Daniel Hernández-Lobato, Kristian Kersting and Anastasia Pentina
  for the fantastic projects we worked on together, and to Kristen Grauman and Adriana
  Kovashka for the exceptional experience working with user studies. I would like
  to thank my colleagues at IST Austria and my office mates who shared their happy
  moods, scientific breakthroughs and thought-provoking conversations with me: Chao,
  Filip, Rustem, Asya, Sameh, Alex, Vlad, Mayu, Neel, Csaba, Thomas, Vladimir, Cristina,
  Alex Z., Avro, Amelie and Emilie, Andreas H. and Andreas E., Chris, Lena, Michael,
  Ali and Ipek, Vera, Igor, Katia. Special thanks to Morten for the countless games
  of table soccer we played together and the tournaments we teamed up for: we will
  definitely win next time:) A very warm hug to Asya for always being so inspiring
  and supportive to me, and for helping me to increase the proportion of female computer
  scientists in our group. "
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Viktoriia
  full_name: Sharmanska, Viktoriia
  id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
  last_name: Sharmanska
  orcid: 0000-0003-0192-9308
citation:
  ama: 'Sharmanska V. Learning with attributes for object recognition: Parametric
    and non-parametrics views. 2015. doi:<a href="https://doi.org/10.15479/at:ista:1401">10.15479/at:ista:1401</a>'
  apa: 'Sharmanska, V. (2015). <i>Learning with attributes for object recognition:
    Parametric and non-parametrics views</i>. Institute of Science and Technology
    Austria. <a href="https://doi.org/10.15479/at:ista:1401">https://doi.org/10.15479/at:ista:1401</a>'
  chicago: 'Sharmanska, Viktoriia. “Learning with Attributes for Object Recognition:
    Parametric and Non-Parametrics Views.” Institute of Science and Technology Austria,
    2015. <a href="https://doi.org/10.15479/at:ista:1401">https://doi.org/10.15479/at:ista:1401</a>.'
  ieee: 'V. Sharmanska, “Learning with attributes for object recognition: Parametric
    and non-parametrics views,” Institute of Science and Technology Austria, 2015.'
  ista: 'Sharmanska V. 2015. Learning with attributes for object recognition: Parametric
    and non-parametrics views. Institute of Science and Technology Austria.'
  mla: 'Sharmanska, Viktoriia. <i>Learning with Attributes for Object Recognition:
    Parametric and Non-Parametrics Views</i>. Institute of Science and Technology
    Austria, 2015, doi:<a href="https://doi.org/10.15479/at:ista:1401">10.15479/at:ista:1401</a>.'
  short: 'V. Sharmanska, Learning with Attributes for Object Recognition: Parametric
    and Non-Parametrics Views, Institute of Science and Technology Austria, 2015.'
date_created: 2018-12-11T11:51:48Z
date_published: 2015-04-01T00:00:00Z
date_updated: 2023-09-07T11:40:11Z
day: '01'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: ChLa
- _id: GradSch
doi: 10.15479/at:ista:1401
file:
- access_level: open_access
  checksum: 3605b402bb6934e09ae4cf672c84baf7
  content_type: application/pdf
  creator: dernst
  date_created: 2021-02-22T11:33:17Z
  date_updated: 2021-02-22T11:33:17Z
  file_id: '9177'
  file_name: 2015_Thesis_Sharmanska.pdf
  file_size: 7964342
  relation: main_file
  success: 1
- access_level: closed
  checksum: e37593b3ee75bf3180629df2d6ca8f4e
  content_type: application/pdf
  creator: cchlebak
  date_created: 2021-11-16T14:40:45Z
  date_updated: 2021-11-17T13:47:24Z
  file_id: '10297'
  file_name: 2015_Thesis_Sharmanska_pdfa.pdf
  file_size: 7372241
  relation: main_file
file_date_updated: 2021-11-17T13:47:24Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- url: http://users.sussex.ac.uk/~nq28/viktoriia/Thesis_Sharmanska.pdf
month: '04'
oa: 1
oa_version: Published Version
page: '144'
publication_identifier:
  issn:
  - 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '5806'
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 with attributes for object recognition: Parametric and non-parametrics
  views'
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2015'
...
---
_id: '2033'
abstract:
- lang: eng
  text: 'The learning with privileged information setting has recently attracted a
    lot of attention within the machine learning community, as it allows the integration
    of additional knowledge into the training process of a classifier, even when this
    comes in the form of a data modality that is not available at test time. Here,
    we show that privileged information can naturally be treated as noise in the latent
    function of a Gaussian process classifier (GPC). That is, in contrast to the standard
    GPC setting, the latent function is not just a nuisance but a feature: it becomes
    a natural measure of confidence about the training data by modulating the slope
    of the GPC probit likelihood function. Extensive experiments on public datasets
    show that the proposed GPC method using privileged noise, called GPC+, improves
    over a standard GPC without privileged knowledge, and also over the current state-of-the-art
    SVM-based method, SVM+. Moreover, we show that advanced neural networks and deep
    learning methods can be compressed as privileged information.'
author:
- first_name: Daniel
  full_name: Hernandez Lobato, Daniel
  last_name: Hernandez Lobato
- first_name: Viktoriia
  full_name: Sharmanska, Viktoriia
  id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
  last_name: Sharmanska
  orcid: 0000-0003-0192-9308
- first_name: Kristian
  full_name: Kersting, Kristian
  last_name: Kersting
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: Novi
  full_name: Quadrianto, Novi
  last_name: Quadrianto
citation:
  ama: 'Hernandez Lobato D, Sharmanska V, Kersting K, Lampert C, Quadrianto N. Mind
    the nuisance: Gaussian process classification using privileged noise. In: <i>Advances
    in Neural Information Processing Systems</i>. Vol 1. Neural Information Processing
    Systems; 2014:837-845.'
  apa: 'Hernandez Lobato, D., Sharmanska, V., Kersting, K., Lampert, C., &#38; Quadrianto,
    N. (2014). Mind the nuisance: Gaussian process classification using privileged
    noise. In <i>Advances in Neural Information Processing Systems</i> (Vol. 1, pp.
    837–845). Montreal, Canada: Neural Information Processing Systems.'
  chicago: 'Hernandez Lobato, Daniel, Viktoriia Sharmanska, Kristian Kersting, Christoph
    Lampert, and Novi Quadrianto. “Mind the Nuisance: Gaussian Process Classification
    Using Privileged Noise.” In <i>Advances in Neural Information Processing Systems</i>,
    1:837–45. Neural Information Processing Systems, 2014.'
  ieee: 'D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, and N. Quadrianto,
    “Mind the nuisance: Gaussian process classification using privileged noise,” in
    <i>Advances in Neural Information Processing Systems</i>, Montreal, Canada, 2014,
    vol. 1, no. January, pp. 837–845.'
  ista: 'Hernandez Lobato D, Sharmanska V, Kersting K, Lampert C, Quadrianto N. 2014.
    Mind the nuisance: Gaussian process classification using privileged noise. Advances
    in Neural Information Processing Systems. NIPS: Neural Information Processing
    Systems vol. 1, 837–845.'
  mla: 'Hernandez Lobato, Daniel, et al. “Mind the Nuisance: Gaussian Process Classification
    Using Privileged Noise.” <i>Advances in Neural Information Processing Systems</i>,
    vol. 1, no. January, Neural Information Processing Systems, 2014, pp. 837–45.'
  short: D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, N. Quadrianto,
    in:, Advances in Neural Information Processing Systems, Neural Information Processing
    Systems, 2014, pp. 837–845.
conference:
  end_date: 2014-12-13
  location: Montreal, Canada
  name: 'NIPS: Neural Information Processing Systems'
  start_date: 2014-12-08
date_created: 2018-12-11T11:55:20Z
date_published: 2014-12-08T00:00:00Z
date_updated: 2023-02-23T10:25:24Z
day: '08'
department:
- _id: ChLa
intvolume: '         1'
issue: January
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://papers.nips.cc/paper/5373-mind-the-nuisance-gaussian-process-classification-using-privileged-noise
month: '12'
oa: 1
oa_version: Submitted Version
page: 837-845
publication: Advances in Neural Information Processing Systems
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '5038'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Mind the nuisance: Gaussian process classification using privileged noise'
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 1
year: '2014'
...
---
_id: '2520'
abstract:
- lang: eng
  text: "We propose a probabilistic model to infer supervised latent variables in\r\nthe
    Hamming space from observed data. Our model allows simultaneous\r\ninference of
    the number of binary latent variables, and their values. The\r\nlatent variables
    preserve neighbourhood structure of the data in a sense\r\nthat objects in the
    same semantic concept have similar latent values, and\r\nobjects in different
    concepts have dissimilar latent values. We formulate\r\nthe supervised infinite
    latent variable problem based on an intuitive\r\nprinciple of pulling objects
    together if they are of the same type, and\r\npushing them apart if they are not.
    We then combine this principle with a\r\nflexible Indian Buffet Process prior
    on the latent variables. We show that\r\nthe inferred supervised latent variables
    can be directly used to perform a\r\nnearest neighbour search for the purpose
    of retrieval.  We introduce a new\r\napplication of dynamically extending hash
    codes, and show how to\r\neffectively couple the structure of the hash codes with
    continuously\r\ngrowing structure of the neighbourhood preserving infinite latent
    feature\r\nspace."
author:
- first_name: Novi
  full_name: Quadrianto, Novi
  last_name: Quadrianto
- first_name: Viktoriia
  full_name: Sharmanska, Viktoriia
  id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
  last_name: Sharmanska
  orcid: 0000-0003-0192-9308
- first_name: David
  full_name: Knowles, David
  last_name: Knowles
- first_name: Zoubin
  full_name: Ghahramani, Zoubin
  last_name: Ghahramani
citation:
  ama: 'Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. The supervised IBP: Neighbourhood
    preserving infinite latent feature models. In: <i>Proceedings of the 29th Conference
    Uncertainty in Artificial Intelligence</i>. AUAI Press; 2013:527-536.'
  apa: 'Quadrianto, N., Sharmanska, V., Knowles, D., &#38; Ghahramani, Z. (2013).
    The supervised IBP: Neighbourhood preserving infinite latent feature models. In
    <i>Proceedings of the 29th conference uncertainty in Artificial Intelligence</i>
    (pp. 527–536). Bellevue, WA, United States: AUAI Press.'
  chicago: 'Quadrianto, Novi, Viktoriia Sharmanska, David Knowles, and Zoubin Ghahramani.
    “The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.”
    In <i>Proceedings of the 29th Conference Uncertainty in Artificial Intelligence</i>,
    527–36. AUAI Press, 2013.'
  ieee: 'N. Quadrianto, V. Sharmanska, D. Knowles, and Z. Ghahramani, “The supervised
    IBP: Neighbourhood preserving infinite latent feature models,” in <i>Proceedings
    of the 29th conference uncertainty in Artificial Intelligence</i>, Bellevue, WA,
    United States, 2013, pp. 527–536.'
  ista: 'Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. 2013. The supervised
    IBP: Neighbourhood preserving infinite latent feature models. Proceedings of the
    29th conference uncertainty in Artificial Intelligence. UAI: Uncertainty in Artificial
    Intelligence, 527–536.'
  mla: 'Quadrianto, Novi, et al. “The Supervised IBP: Neighbourhood Preserving Infinite
    Latent Feature Models.” <i>Proceedings of the 29th Conference Uncertainty in Artificial
    Intelligence</i>, AUAI Press, 2013, pp. 527–36.'
  short: N. Quadrianto, V. Sharmanska, D. Knowles, Z. Ghahramani, in:, Proceedings
    of the 29th Conference Uncertainty in Artificial Intelligence, AUAI Press, 2013,
    pp. 527–536.
conference:
  end_date: 2013-07-15
  location: Bellevue, WA, United States
  name: 'UAI: Uncertainty in Artificial Intelligence'
  start_date: 2013-07-11
date_created: 2018-12-11T11:58:09Z
date_published: 2013-07-11T00:00:00Z
date_updated: 2023-02-23T10:46:36Z
day: '11'
ddc:
- '000'
department:
- _id: ChLa
file:
- access_level: open_access
  checksum: 325f20c4b926bd74d39006b97df572bd
  content_type: application/pdf
  creator: system
  date_created: 2018-12-12T10:15:16Z
  date_updated: 2020-07-14T12:45:42Z
  file_id: '5134'
  file_name: IST-2013-137-v1+1_QuaShaKnoGha13.pdf
  file_size: 1117100
  relation: main_file
file_date_updated: 2020-07-14T12:45:42Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
page: 527 - 536
publication: Proceedings of the 29th conference uncertainty in Artificial Intelligence
publication_identifier:
  isbn:
  - '9780974903996'
publication_status: published
publisher: AUAI Press
publist_id: '4381'
pubrep_id: '137'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'The supervised IBP: Neighbourhood preserving infinite latent feature models'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2293'
abstract:
- lang: eng
  text: Many computer vision problems have an asymmetric distribution of information
    between training and test time. In this work, we study the case where we are given
    additional information about the training data, which however will not be available
    at test time. This situation is called learning using privileged information (LUPI).
    We introduce two maximum-margin techniques that are able to make use of this additional
    source of information, and we show that the framework is applicable to several
    scenarios that have been studied in computer vision before. Experiments with attributes,
    bounding boxes, image tags and rationales as additional information in object
    classification show promising results.
author:
- first_name: Viktoriia
  full_name: Sharmanska, Viktoriia
  id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
  last_name: Sharmanska
  orcid: 0000-0003-0192-9308
- first_name: Novi
  full_name: Quadrianto, Novi
  last_name: Quadrianto
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Sharmanska V, Quadrianto N, Lampert C. Learning to rank using privileged information.
    In: IEEE; 2013:825-832. doi:<a href="https://doi.org/10.1109/ICCV.2013.107">10.1109/ICCV.2013.107</a>'
  apa: 'Sharmanska, V., Quadrianto, N., &#38; Lampert, C. (2013). Learning to rank
    using privileged information (pp. 825–832). Presented at the ICCV: International
    Conference on Computer Vision, Sydney, Australia: IEEE. <a href="https://doi.org/10.1109/ICCV.2013.107">https://doi.org/10.1109/ICCV.2013.107</a>'
  chicago: Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Learning
    to Rank Using Privileged Information,” 825–32. IEEE, 2013. <a href="https://doi.org/10.1109/ICCV.2013.107">https://doi.org/10.1109/ICCV.2013.107</a>.
  ieee: 'V. Sharmanska, N. Quadrianto, and C. Lampert, “Learning to rank using privileged
    information,” presented at the ICCV: International Conference on Computer Vision,
    Sydney, Australia, 2013, pp. 825–832.'
  ista: 'Sharmanska V, Quadrianto N, Lampert C. 2013. Learning to rank using privileged
    information. ICCV: International Conference on Computer Vision, 825–832.'
  mla: Sharmanska, Viktoriia, et al. <i>Learning to Rank Using Privileged Information</i>.
    IEEE, 2013, pp. 825–32, doi:<a href="https://doi.org/10.1109/ICCV.2013.107">10.1109/ICCV.2013.107</a>.
  short: V. Sharmanska, N. Quadrianto, C. Lampert, in:, IEEE, 2013, pp. 825–832.
conference:
  end_date: 2013-12-08
  location: Sydney, Australia
  name: 'ICCV: International Conference on Computer Vision'
  start_date: 2013-12-01
date_created: 2018-12-11T11:56:49Z
date_published: 2013-12-01T00:00:00Z
date_updated: 2023-02-23T10:36:41Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCV.2013.107
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: www.cv-foundation.org/openaccess/content_iccv_2013/papers/Sharmanska_Learning_to_Rank_2013_ICCV_paper.pdf
month: '12'
oa: 1
oa_version: Submitted Version
page: 825 - 832
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '308036'
  name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: IEEE
publist_id: '4635'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning to rank using privileged information
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '3125'
abstract:
- lang: eng
  text: We propose a new learning method to infer a mid-level feature representation
    that combines the advantage of semantic attribute representations with the higher
    expressive power of non-semantic features. The idea lies in augmenting an existing
    attribute-based representation with additional dimensions for which an autoencoder
    model is coupled with a large-margin principle. This construction allows a smooth
    transition between the zero-shot regime with no training example, the unsupervised
    regime with training examples but without class labels, and the supervised regime
    with training examples and with class labels. The resulting optimization problem
    can be solved efficiently, because several of the necessity steps have closed-form
    solutions. Through extensive experiments we show that the augmented representation
    achieves better results in terms of object categorization accuracy than the semantic
    representation alone.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Viktoriia
  full_name: Sharmanska, Viktoriia
  id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
  last_name: Sharmanska
  orcid: 0000-0003-0192-9308
- first_name: Novi
  full_name: Quadrianto, Novi
  last_name: Quadrianto
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Sharmanska V, Quadrianto N, Lampert C. Augmented attribute representations.
    In: Vol 7576. Springer; 2012:242-255. doi:<a href="https://doi.org/10.1007/978-3-642-33715-4_18">10.1007/978-3-642-33715-4_18</a>'
  apa: 'Sharmanska, V., Quadrianto, N., &#38; Lampert, C. (2012). Augmented attribute
    representations (Vol. 7576, pp. 242–255). Presented at the ECCV: European Conference
    on Computer Vision, Florence, Italy: Springer. <a href="https://doi.org/10.1007/978-3-642-33715-4_18">https://doi.org/10.1007/978-3-642-33715-4_18</a>'
  chicago: Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Augmented
    Attribute Representations,” 7576:242–55. Springer, 2012. <a href="https://doi.org/10.1007/978-3-642-33715-4_18">https://doi.org/10.1007/978-3-642-33715-4_18</a>.
  ieee: 'V. Sharmanska, N. Quadrianto, and C. Lampert, “Augmented attribute representations,”
    presented at the ECCV: European Conference on Computer Vision, Florence, Italy,
    2012, vol. 7576, no. PART 5, pp. 242–255.'
  ista: 'Sharmanska V, Quadrianto N, Lampert C. 2012. Augmented attribute representations.
    ECCV: European Conference on Computer Vision, LNCS, vol. 7576, 242–255.'
  mla: Sharmanska, Viktoriia, et al. <i>Augmented Attribute Representations</i>. Vol.
    7576, no. PART 5, Springer, 2012, pp. 242–55, doi:<a href="https://doi.org/10.1007/978-3-642-33715-4_18">10.1007/978-3-642-33715-4_18</a>.
  short: V. Sharmanska, N. Quadrianto, C. Lampert, in:, Springer, 2012, pp. 242–255.
conference:
  end_date: 2012-10-13
  location: Florence, Italy
  name: 'ECCV: European Conference on Computer Vision'
  start_date: 2012-10-07
date_created: 2018-12-11T12:01:32Z
date_published: 2012-10-01T00:00:00Z
date_updated: 2023-02-23T11:13:25Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1007/978-3-642-33715-4_18
file:
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  creator: dernst
  date_created: 2020-05-15T12:29:04Z
  date_updated: 2020-07-14T12:46:00Z
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file_date_updated: 2020-07-14T12:46:00Z
has_accepted_license: '1'
intvolume: '      7576'
issue: PART 5
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
page: 242 - 255
publication_status: published
publisher: Springer
publist_id: '3574'
quality_controlled: '1'
scopus_import: 1
status: public
title: Augmented attribute representations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 7576
year: '2012'
...