Catastrophic Overfitting is Weight Drift the Training Loss Cannot See
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Fast adversarial training (FAT) makes robust training affordable by replacing the multi-step attack with a single-step one, but it suffers from catastrophic overfitting (CO): partway through training,...
arXiv:2606. 00738v1 Announce Type: cross Abstract: Adversarial Training (AT) is a leading defense against adversarial examples but often suffers from Catastrophic Overfitting (CO) in efficient single-step variants, where robustness to multi-step attacks collapses despite high single-step performance.
arXiv:2505. 02360v3 Announce Type: replace-cross Abstract: Adversarial training is a cornerstone of robust deep learning, but fast methods like the Fast Gradient Sign Method (FGSM) often suffer from Catastrophic Overfitting (CO), where models become robust to single-step attacks but fail against multi-step variants.
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The paper introduces Median Temporal Ensembling, a training‑free aggregation method for action‑chunked visuomotor policies that replaces the standard exponential weighted mean with a coordinate‑wise median. This approach remains robust against adversarial corruption, maintaining a high recovery rate even as attack strength increases, and performs at least as well as the mean across numerous configurations while improving in many cases. It also handles non‑adversarial failures such as blank camera frames and shows limited impact on clean data, though it cannot counteract uniform shifts applied to all predictions.
arXiv:2608. 06177v1 Announce Type: new Abstract: Binary neural networks are very attractive for constrained deployment, enabling small footprint and low-power inference.