# One-Class Feature Learning Using Intra-Class Splitting

arXiv: https://arxiv.org/abs/1812.08468 · HTML: https://mlsanity.com/paper/1812.08468/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [One-Class Classification: A Survey](https://mlsanity.com/paper/2101.03064/) (2021)
- [Anomaly Detection Based on Selection and Weighting in Latent Space](https://mlsanity.com/paper/2103.04662/) (2021)

## Abstract (arXiv)

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.