Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries Article Swipe
Kun-Peng Ning
,
Lue Tao
,
Songcan Chen
,
Sheng-Jun Huang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v35i10.17106
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v35i10.17106
In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improving the model robustness by training with noise perturbations. Most existing studies assume a fixed perturbation level for all training examples, which however hardly holds in real tasks. In fact, excessive perturbations may destroy the discriminative content of an example, while deficient perturbations may fail to provide helpful information for improving the robustness. Motivated by this observation, we propose to adaptively adjust the perturbation levels for each example in the training process. Specifically, a novel active learning framework is proposed to allow the model interactively querying the correct perturbation level from human experts. By designing a cost-effective sampling strategy along with a new query type, the robustness can be significantly improved with a few queries. Both theoretical analysis and experimental studies validate the effectiveness of the proposed approach.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v35i10.17106
- https://ojs.aaai.org/index.php/AAAI/article/download/17106/16913
- OA Status
- diamond
- Cited By
- 8
- References
- 85
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3140223115
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3140223115Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v35i10.17106Digital Object Identifier
- Title
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Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active QueriesWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-05-18Full publication date if available
- Authors
-
Kun-Peng Ning, Lue Tao, Songcan Chen, Sheng-Jun HuangList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v35i10.17106Publisher landing page
- PDF URL
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https://ojs.aaai.org/index.php/AAAI/article/download/17106/16913Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/17106/16913Direct OA link when available
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Robustness (evolution), Computer science, Discriminative model, Perturbation (astronomy), Machine learning, Artificial intelligence, Gene, Physics, Biochemistry, Quantum mechanics, ChemistryTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 4, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
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85Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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