On adversarial training and the 1 Nearest Neighbor classifier

arXiv:2404.06313v1 Announce Type: new
Abstract: The ability to fool deep learning classifiers with tiny perturbations of the input has lead to the development of adversarial training in which the loss with respect to adversarial examples is minimized in addition to the training examples. While adversarial training improves the robustness of the learned classifiers, the procedure is computationally expensive, sensitive to hyperparameters and may still leave the classifier vulnerable to other types of small perturbations. In this paper we analyze the …

adversarial adversarial training arxiv classifier cs.lg training type