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One-class recognition is traditionally approached either as a representationlearning problem or a feature modeling problem. In this work, we argue thatboth of these approaches have their own limitations; and a more effectivesolution can be obtained by combining the two. The proposed approach is basedon the combination of a generative framework and a one-class classificationmethod. First, we learn generative features using the one-class data with agenerative framework. We augment the learned features with the correspondingreconstruction errors to obtain augmented features. Then, we qualitativelyidentify a suitable feature distribution that reduces the redundancy in thechosen classifier space. Finally, we force the augmented features to take theform of this distribution using an adversarial framework. We test theeffectiveness of the proposed method on three one-class classification tasksand obtain state-of-the-art results.

A Joint Representation Learning and Feature Modeling Approach for One-class Recognition