Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust? 事件

Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust? arXiv:2605.20606v2 Announce Type: replace Abstract: Dataset distillation (DD) compresses a large training set into a small synthetic set for efficient training, but most DD methods optimize only clean accuracy and leave robustness uncontrolled. Recent robust DD methods improve robustness, yet they often suffer from a poor accuracy-robustness trade-off because they (i) treat all adversarially perturbed examples uniformly, d