Adversarial Neural Pruning

ICLR 2020 Anonymous

Despite the remarkable performance of deep neural networks (DNNs) on various tasks, they are susceptible to adversarial perturbations which makes it difficult to deploy them in real-world safety-critical applications. In this paper, we aim to obtain robust networks by sparsifying DNN's latent features sensitive to adversarial perturbation... (read more)

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