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Found 3 papers, 1 papers with code
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement
However, most AT methods suffer from robust overfitting, i. e., a significant generalization gap in adversarial robustness between the training and testing curves.
On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
Contemporary machine learning requires training large neural networks on massive datasets and thus faces the challenges of high computational demands.
Stable and Efficient Adversarial Training through Local Linearization
There has been a recent surge in single-step adversarial training as it shows robustness and efficiency.
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