Learning Adversarially Robust Sparse Networks via Weight Reparameterization

Although increasing model size can enhance the adversarial robustness of deep neural networks, in resource-constrained environments, there exist critical sparsity constraints. While the recent robust pruning technologies show promising direction to obtain adversarially robust sparse networks, they perform poorly with high sparsity. In this work, we bridge this performance gap by reparameterizing network parameters to simultaneously learn the sparse structure and the robustness. Specifically, we introduce Twin-Rep, which reparameterizes original weights into the product of two factors during training and performs pruning on the reparameterized weights to satisfy the target sparsity constraint. Twin-Rep implicitly adds the sparsity constraint without changing the robust training objective, thus can enhance robustness under high sparsity. We also introduce another variant of weight reparameterization for better channel pruning. When inferring, we restore the original weight structure to obtain compact and robust networks. Extensive experiments on diverse datasets demonstrate that our method achieves state-of-the-art results, outperforming the current sparse robust training method and robustness-aware pruning method. Our code is available at https://github.com/UCAS-LCH/Twin-Rep.