# Sparse Representation-based Open Set Recognition

arXiv: https://arxiv.org/abs/1705.02431 · HTML: https://mlsanity.com/paper/1705.02431/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2017.

## Built on by (most cited first, 30 of 36)

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Adversarial Reciprocal Points Learning for Open Set Recognition](https://mlsanity.com/paper/2103.00953/) (2021)
- [Learning Placeholders for Open-Set Recognition](https://mlsanity.com/paper/2103.15086/) (2021)
- [Expanding Low-Density Latent Regions for Open-Set Object Detection](https://mlsanity.com/paper/2203.14911/) (2022)
- [One-Class Classification: A Survey](https://mlsanity.com/paper/2101.03064/) (2021)
- [Open-world Machine Learning: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2105.13448/) (2021)
- [Class-Specific Semantic Reconstruction for Open Set Recognition](https://mlsanity.com/paper/2207.02158/) (2022)
- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [Learning a Self-Expressive Network for Subspace Clustering](https://mlsanity.com/paper/2110.04318/) (2021)
- [Difficulty-Aware Simulator for Open Set Recognition](https://mlsanity.com/paper/2207.10024/) (2022)
- [Open-set Adversarial Defense with Clean-Adversarial Mutual Learning](https://mlsanity.com/paper/2202.05953/) (2022)
- [Conditional Variational Capsule Network for Open Set Recognition](https://mlsanity.com/paper/2104.09159/) (2021)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Open World Object Detection: A Survey](https://mlsanity.com/paper/2410.11301/) (2024)
- [M2IOSR: Maximal Mutual Information Open Set Recognition](https://mlsanity.com/paper/2108.02373/) (2021)
- [Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry](https://mlsanity.com/paper/2207.09963/) (2022)
- [Collective Decision of One-vs-Rest Networks for Open Set Recognition](https://mlsanity.com/paper/2103.10230/) (2021)
- [Glocal Energy-based Learning for Few-Shot Open-Set Recognition](https://mlsanity.com/paper/2304.11855/) (2023)
- [Conditional Extreme Value Theory for Open Set Video Domain Adaptation](https://mlsanity.com/paper/2109.00522/) (2021)
- [Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery](https://mlsanity.com/paper/2409.19659/) (2024)
- [A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection](https://mlsanity.com/paper/2209.12935/) (2022)
- [Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach](https://mlsanity.com/paper/2502.19691/) (2025)
- [Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios](https://mlsanity.com/paper/2105.07635/) (2021)
- [Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition](https://mlsanity.com/paper/2103.12871/) (2021)
- [Open-Set Recognition of Breast Cancer Treatments](https://mlsanity.com/paper/2201.02923/) (2022)
- [Deep Open-Set Recognition for Silicon Wafer Production Monitoring](https://mlsanity.com/paper/2208.14071/) (2022)
- [Reconstructing Humpty Dumpty: Multi-feature Graph Autoencoder for Open Set Action Recognition](https://mlsanity.com/paper/2212.06023/) (2022)
- [UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework](https://mlsanity.com/paper/2412.09229/) (2024)
- [Novel Category Discovery with X-Agent Attention for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2509.01275/) (2025)
- [A Discriminative Vectorial Framework for Multi-modal Feature Representation](https://mlsanity.com/paper/2103.05597/) (2021)

## Abstract (arXiv)

We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms. Code is public available: https://github.com/hezhangsprinter/SROSR