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arXiv · 1903.04774

Open-Set Recognition Using Intra-Class Splitting

Patrick Schlachter·Yiwen Liao·Bin Yang
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Abstract

This paper proposes a method to use deep neural networks as end-to-end open-set classifiers. It is based on intra-class data splitting. In open-set recognition, only samples from a limited number of known classes are available for training. During inference, an open-set classifier must reject samples from unknown classes while correctly classifying samples from known classes. The proposed method splits given data into typical and atypical normal subsets by using a closed-set classifier. This enables to model the abnormal classes by atypical normal samples. Accordingly, the open-set recognition problem is reformulated into a traditional classification problem. In addition, a closed-set regularization is proposed to guarantee a high closed-set classification performance. Intensive experiments on five well-known image datasets showed the effectiveness of the proposed method which outperformed the baselines and achieved a distinct improvement over the state-of-the-art methods.

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Patrick Schlachter, Yiwen Liao, Bin Yang. 2019-03-12. Open-Set Recognition Using Intra-Class Splitting. https://doi.org/10.23919/eusipco.2019.8902738

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