Khaled A. Harras

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.