  One-class recognition is traditionally approached either as a representation
learning problem or a feature modeling problem. In this work, we argue that
both of these approaches have their own limitations; and a more effective
solution can be obtained by combining the two. The proposed approach is based
on the combination of a generative framework and a one-class classification
method. First, we learn generative features using the one-class data with a
generative framework. We augment the learned features with the corresponding
reconstruction errors to obtain augmented features. Then, we qualitatively
identify a suitable feature distribution that reduces the redundancy in the
chosen classifier space. Finally, we force the augmented features to take the
form of this distribution using an adversarial framework. We test the
effectiveness of the proposed method on three one-class classification tasks
and obtain state-of-the-art results.
