# Deep One-Class Classification Using Intra-Class Splitting

arXiv: https://arxiv.org/abs/1902.01194 · HTML: https://mlsanity.com/paper/1902.01194/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Margin-Aware Intra-Class Novelty Identification for Medical Images](https://mlsanity.com/paper/2108.00117/) (2021)
- [Anomaly Detection Based on Selection and Weighting in Latent Space](https://mlsanity.com/paper/2103.04662/) (2021)
- [Deep Feature Selection Using a Novel Complementary Feature Mask](https://mlsanity.com/paper/2209.12282/) (2022)
- [Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities](https://mlsanity.com/paper/2210.17440/) (2022)

## Abstract (arXiv)

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.