  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. We argue that classifiers
inspired by the statistical perspective in generative or discriminative models
are more effective than existing approaches, such as a normality score from a
surrogate classifier. We thoroughly evaluate different self-supervised
representation learning algorithms under the proposed framework for one-class
classification. Moreover, we present a novel distribution-augmented contrastive
learning that extends training distributions via data augmentation to obstruct
the uniformity of contrastive representations. In experiments, we demonstrate
state-of-the-art performance on visual domain one-class classification
benchmarks, including novelty and anomaly detection. Finally, we present visual
explanations, confirming that the decision-making process of deep one-class
classifiers is intuitive to humans. The code is available at
https://github.com/google-research/deep_representation_one_class.
