arXiv AI
Jun 29

Halt Fast! Early Stopping for Certified Robustness

arXiv:2606. 27694v1 Announce Type: cross Abstract: Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited by extreme computational costs.

By Andrew C. Cullen, Paul Montague, Benjamin I. P. Rubinstein
arXiv Computer Vision
5d ago

Sparsity-Adaptive Sharpness-Aware Minimization

The paper introduces Sparsity-Adaptive Sharpness-Aware Minimization (SA‑SAM), a method that adjusts the perturbation radius in sharpness-aware training to remain consistent as model sparsity increases. It also evaluates a Magnitude‑Weighted Hessian (MWH) importance metric derived from second‑order analysis. Experiments on CIFAR‑10‑C, CIFAR‑100‑C, and ImageNet‑100‑C show that SA‑SAM improves corruption robustness at 80–90% sparsity while maintaining clean accuracy, and the study reports inference throughput at deployment‑relevant sparsity levels.

By Shiryu Ueno, Yoshikazu Hayashi, Kunihito Kato