Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks

ICLR 2019 Kimin LeeSukmin YunKibok LeeHonglak LeeBo LiJinwoo Shin

Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks poorly generalize from such noisy training datasets. In this paper, we propose a novel inference method, Deep Determinantal Generative Classifier (DDGC), which can obtain a more robust decision boundary under any softmax neural classifier pre-trained on noisy datasets... (read more)

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