--- _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 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: 21st International Conference on Runtime Verification. Vol 12974. Cham: Springer Nature; 2021:42-61. doi:10.1007/978-3-030-88494-9_3' apa: 'Lukina, A., Schilling, C., & Henzinger, T. A. (2021). Into the unknown: active monitoring of neural networks. In 21st International Conference on Runtime Verification (Vol. 12974, pp. 42–61). Cham: Springer Nature. https://doi.org/10.1007/978-3-030-88494-9_3' chicago: 'Lukina, Anna, Christian Schilling, and Thomas A Henzinger. “Into the Unknown: Active Monitoring of Neural Networks.” In 21st International Conference on Runtime Verification, 12974:42–61. Cham: Springer Nature, 2021. https://doi.org/10.1007/978-3-030-88494-9_3.' ieee: 'A. Lukina, C. Schilling, and T. A. Henzinger, “Into the unknown: active monitoring of neural networks,” in 21st International Conference on Runtime Verification, 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.” 21st International Conference on Runtime Verification, vol. 12974, Springer Nature, 2021, pp. 42–61, doi:10.1007/978-3-030-88494-9_3.' 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' ...