Conserve-update-revise to cure generalization and robustness trade-off in adversarial training
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Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training. Presented at 12th International Conference on Learning Representations.
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There exists an inherent trade-off between a model's ability to generalize to unseen data and its robustness against adversarial perturbations.
“Research from the broader computer vision field suggests that there exists an inherent trade-off between a model's ability to generalize to unseen data and its robustness against adversarial perturbations [219-222].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [222]
The Introduction (p. 1) says adversarial training often compromises performance on clean images, creating a trade-off between standard and adversarial generalization. It does not establish that the trade-off is inherent or unavoidable; the paper reports that CURE improves the balance between the two (Figure 1, p. 2). (LLM, conf=0.87)
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- Authors
- Gowda, S., Arani, E., & Zonooz, B.
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2024-05-07
- OpenAlex
- W7223007174
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Funders
- European Commission (101120657)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- adversarial training, robustness, generalization, adversarial robustness, model updating
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