Learning and Evaluating Representations for Deep One-class Classification
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We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. The framework not only allows to learn better representations, but also permits building one-class classifiers that are faithful to the target task. In particular, we present a novel distribution-augmented contrastive learning that extends training distributions via data augmentation to obstruct the uniformity of contrastive representations. Moreover, we argue that classifiers inspired by the statistical perspective in generative or discriminative models are more effective than existing approaches, such as an average of normality scores from a surrogate classifier. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks. Finally, we present visual explanations, confirming that the decision-making process of our deep one-class classifier is intuitive to humans. The code is available at: https://github.com/google-research/google-research/tree/master/deep_representation_one_class.
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