One-Class Feature Learning Using Intra-Class Splitting

被引:0
作者
Schlachter, Patrick [1 ]
Liao, Yiwen [1 ]
Yang, Bin [1 ]
机构
[1] Univ Stuttgart, Inst Signal Proc & Syst Theory, Stuttgart, Germany
来源
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2019年
关键词
SUPPORT;
D O I
10.23919/eusipco.2019.8902848
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one class classification, feature learning is challenging, because only samples of one class are available during training. Hence, state-of-the-art methods require reference multi-class datasets to pretrain feature extractors. In contrast, the proposed method realizes feature learning by splitting the given normal class into typical and atypical normal samples. By introducing closeness loss and dispersion loss, an intra-class joint training procedure between the two subsets after splitting enables the extraction of valuable features for one-class classification. Various experiments on three well-known image classification datasets demonstrate the effectiveness of our method which outperformed other baseline models in average.
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页数:5
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