Conference
Open access
Sep 2026
Diverge to Converge: Mutual Heterogeneous Learning for Robust Pruning
Mutual Heterogeneous Learning (MHL) is proposed, a framework enabling robust pruning via single-model inference that significantly outperforms single-model baselines in both adversarial robustness and corruption robustness, while maintaining competitive clean accuracy.
Jin-Hui Yu, Zikai Zhang, Khaled A. Harras et al.
· Proceedings of the Thirty-Fi... · 0 citations