Deep One-Class Classification Using Data Splitting
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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 seeked which conventionally trained 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 additional distance constraints on the latent feature space. Various experiments on three well-known image datasets showed the effectiveness of the proposed method which outperformed seven baseline models in 23 of 30 experiments.
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