Jinhui Yu

Diverge to Converge: Mutual Heterogeneous Learning for Robust Pruning

Neural network pruning is crucial for efficient deployment on resource-constrained devices, yet achieving high sparsity often leads to significant robustness degradation against adversarial perturbations and corruptions. Recent works typically rely on single-model fine-tuning along a fixed optimizat