Author

Zikai Zhang

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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.

Jinhui Yu, Zikai Zhang, Khaled A. Harras et al. · 0 citations