  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.
