2 papers
cs.LG2026
When Muon Optimizer Meets Adversarial Training: A Theoretical and Empirical Study
Jun Yan, Weiquan Huang, Jiankai Zuo +4
Adversarial training (AT) remains one of the most reliable empirical defenses against adversarial attacks. Its robustness critically depends on how the underlying min-max objective…
cs.CV2025
Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency
Yanbiao Ma, Wei Dai, Bowei Liu +5
Despite the fast progress of deep learning, one standing challenge is the gap of the observed training samples and the underlying true distribution. There are multiple reasons for…