Research map / Adversarial robustness and security / Certified and empirical robustness
Exploring The Forgetting in Adversarial Training: A Novel Method for Enhancing Robustness
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Proposes a new adversarial training method that addresses catastrophic forgetting in AT to improve clean and robust accuracy.
Problem
Adversarial training still leaves a gap in achieving strong clean and robust accuracy on many practical tasks; the paper views AT through the lens of catastrophic forgetting.
Approach
Connects adversarial training to catastrophic forgetting and uses this perspective to design a new AT method for enhancing robustness (details not available in the abstract provided).
Experimental setup
Metrics: clean accuracy robust accuracy
Builds on: adversarial training
Closest papers by meaning
- The Effectiveness of Random Forgetting for Robust Generalization (ICLR 2024 · similarity 0.92)
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- A comprehensive, application-oriented study of catastrophic forgetting in DNNs (ICLR 2019 · similarity 0.90)
- Towards Understanding Catastrophic Forgetting in Two-layer Convolutional Neural Networks (ICML 2025 · similarity 0.89)
- Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data (ICLR 2025 · similarity 0.89)
- Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning (ICLR 2025 · similarity 0.88)
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