---
_id: '10206'
abstract:
- lang: eng
  text: Neural-network classifiers achieve high accuracy when predicting the class
    of an input that they were trained to identify. Maintaining this accuracy in dynamic
    environments, where inputs frequently fall outside the fixed set of initially
    known classes, remains a challenge. The typical approach is to detect inputs from
    novel classes and retrain the classifier on an augmented dataset. However, not
    only the classifier but also the detection mechanism needs to adapt in order to
    distinguish between newly learned and yet unknown input classes. To address this
    challenge, we introduce an algorithmic framework for active monitoring of a neural
    network. A monitor wrapped in our framework operates in parallel with the neural
    network and interacts with a human user via a series of interpretable labeling
    queries for incremental adaptation. In addition, we propose an adaptive quantitative
    monitor to improve precision. An experimental evaluation on a diverse set of benchmarks
    with varying numbers of classes confirms the benefits of our active monitoring
    framework in dynamic scenarios.
acknowledgement: We thank Christoph Lampert and Alex Greengold for fruitful discussions.
  This research was supported in part by the Simons Institute for the Theory of Computing,
  the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award), and the
  European Union’s Horizon 2020 research and innovation programme under the Marie
  Skłodowska-Curie grant agreement No. 754411.
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Anna
  full_name: Lukina, Anna
  id: CBA4D1A8-0FE8-11E9-BDE6-07BFE5697425
  last_name: Lukina
- first_name: Christian
  full_name: Schilling, Christian
  id: 3A2F4DCE-F248-11E8-B48F-1D18A9856A87
  last_name: Schilling
  orcid: 0000-0003-3658-1065
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000-0002-2985-7724
citation:
  ama: 'Lukina A, Schilling C, Henzinger TA. Into the unknown: active monitoring of neural
    networks. In: <i>21st International Conference on Runtime Verification</i>. Vol
    12974. Cham: Springer Nature; 2021:42-61. doi:<a href="https://doi.org/10.1007/978-3-030-88494-9_3">10.1007/978-3-030-88494-9_3</a>'
  apa: 'Lukina, A., Schilling, C., &#38; Henzinger, T. A. (2021). Into the unknown:
    active monitoring of neural networks. In <i>21st International Conference on Runtime
    Verification</i> (Vol. 12974, pp. 42–61). Cham: Springer Nature. <a href="https://doi.org/10.1007/978-3-030-88494-9_3">https://doi.org/10.1007/978-3-030-88494-9_3</a>'
  chicago: 'Lukina, Anna, Christian Schilling, and Thomas A Henzinger. “Into the Unknown:
    Active Monitoring of Neural Networks.” In <i>21st International Conference on
    Runtime Verification</i>, 12974:42–61. Cham: Springer Nature, 2021. <a href="https://doi.org/10.1007/978-3-030-88494-9_3">https://doi.org/10.1007/978-3-030-88494-9_3</a>.'
  ieee: 'A. Lukina, C. Schilling, and T. A. Henzinger, “Into the unknown: active monitoring
    of neural networks,” in <i>21st International Conference on Runtime Verification</i>,
    Virtual, 2021, vol. 12974, pp. 42–61.'
  ista: 'Lukina A, Schilling C, Henzinger TA. 2021. Into the unknown: active monitoring
    of neural networks. 21st International Conference on Runtime Verification. RV:
    Runtime Verification, LNCS, vol. 12974, 42–61.'
  mla: 'Lukina, Anna, et al. “Into the Unknown: Active Monitoring of Neural Networks.”
    <i>21st International Conference on Runtime Verification</i>, vol. 12974, Springer
    Nature, 2021, pp. 42–61, doi:<a href="https://doi.org/10.1007/978-3-030-88494-9_3">10.1007/978-3-030-88494-9_3</a>.'
  short: A. Lukina, C. Schilling, T.A. Henzinger, in:, 21st International Conference
    on Runtime Verification, Springer Nature, Cham, 2021, pp. 42–61.
conference:
  end_date: 2021-10-14
  location: Virtual
  name: 'RV: Runtime Verification'
  start_date: 2021-10-11
date_created: 2021-10-31T23:01:31Z
date_published: 2021-10-06T00:00:00Z
date_updated: 2024-01-30T12:06:56Z
day: '06'
department:
- _id: ToHe
doi: 10.1007/978-3-030-88494-9_3
ec_funded: 1
external_id:
  arxiv:
  - '2009.06429'
  isi:
  - '000719383800003'
isi: 1
keyword:
- monitoring
- neural networks
- novelty detection
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2009.06429
month: '10'
oa: 1
oa_version: Preprint
page: 42-61
place: Cham
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '754411'
  name: ISTplus - Postdoctoral Fellowships
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: Z211
  name: The Wittgenstein Prize
publication: 21st International Conference on Runtime Verification
publication_identifier:
  eisbn:
  - 978-3-030-88494-9
  eissn:
  - 1611-3349
  isbn:
  - 9-783-0308-8493-2
  issn:
  - 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
  record:
  - id: '13234'
    relation: extended_version
    status: public
scopus_import: '1'
status: public
title: 'Into the unknown: active monitoring of neural networks'
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: '12974 '
year: '2021'
...