# Open-Set Recognition Using Intra-Class Splitting

arXiv: https://arxiv.org/abs/1903.04774 · HTML: https://mlsanity.com/paper/1903.04774/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Free Lunch for Gait Recognition: A Novel Relation Descriptor](https://mlsanity.com/paper/2308.11487/) (2023)
- [A Survey on Open-Set Image Recognition](https://mlsanity.com/paper/2312.15571/) (2023)
- [LORD: Leveraging Open-Set Recognition with Unknown Data](https://mlsanity.com/paper/2308.12584/) (2023)
- [LLM meets Vision-Language Models for Zero-Shot One-Class Classification](https://mlsanity.com/paper/2404.00675/) (2024)
- [Class Relevance Learning For Out-of-distribution Detection](https://mlsanity.com/paper/2401.01021/) (2023)

## Abstract (arXiv)

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.