  This paper introduces a generic method which enables to use conventional deep
neural networks as end-to-end one-class classifiers. The method is based on
splitting given data from one class into two subsets. In one-class
classification, only samples of one normal class are available for training.
During inference, a closed and tight decision boundary around the training
samples is sought which conventional binary or multi-class neural networks are
not able to provide. By splitting data into typical and atypical normal
subsets, the proposed method can use a binary loss and defines an auxiliary
subnetwork for distance constraints in the latent space. Various experiments on
three well-known image datasets showed the effectiveness of the proposed method
which outperformed seven baselines and had a better or comparable performance
to the state-of-the-art.
