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arXiv · 1902.01194

Deep One-Class Classification Using Intra-Class Splitting

Patrick Schlachter·Yiwen Liao·Bin Yang
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Abstract

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.

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Patrick Schlachter, Yiwen Liao, Bin Yang. 2019-09-16. Deep One-Class Classification Using Intra-Class Splitting. https://doi.org/10.1109/dsw.2019.8755576

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