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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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2 results for “Auto-Attack”

One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Trainingcs.LG

Proposes SOVR loss to improve adversarial robustness by increasing logit margins.

problem Adversarial training's difficulty in robustness against sophisticated attacks.

method Introduces SOVR loss function that switches from cross-entropy to one-vs-the-rest loss for important samples.

result SOVR loss increases logit margins of important samples, improving robustness against Auto-Attack.

adversarial training robustness logit margins one-vs-the-rest loss Auto-Attack

2022-07-21abs ↗pdf ↗

Standard-Deviation-Inspired Regularization for Improving Adversarial Robustnesscs.LG

This work proposes SDI regularization to improve adversarial robustness.

problem Improving adversarial robustness of deep neural networks.

method SDI regularization term to complement adversarial training.

result Combining SDI with AT variants enhances robustness and generalization.

Adversarial Training Adversarial Robustness Deep Neural Networks Standard Deviation Regularization

2024-12-27abs ↗pdf ↗