# Learning a Neural-network-based Representation for Open Set Recognition

arXiv: https://arxiv.org/abs/1802.04365 · HTML: https://mlsanity.com/paper/1802.04365/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

## Built on by (most cited first, 30 of 40)

- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [Open Domain Generalization with Domain-Augmented Meta-Learning](https://mlsanity.com/paper/2104.03620/) (2021)
- [Open-world Machine Learning: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2105.13448/) (2021)
- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach](https://mlsanity.com/paper/2110.04514/) (2021)
- [Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications](https://mlsanity.com/paper/2104.03182/) (2021)
- [Open World Object Detection: A Survey](https://mlsanity.com/paper/2410.11301/) (2024)
- [Glocal Energy-based Learning for Few-Shot Open-Set Recognition](https://mlsanity.com/paper/2304.11855/) (2023)
- [MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition](https://mlsanity.com/paper/2305.01245/) (2023)
- [LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition](https://mlsanity.com/paper/2309.12780/) (2023)
- [A Survey on Open-Set Image Recognition](https://mlsanity.com/paper/2312.15571/) (2023)
- [Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss](https://mlsanity.com/paper/2311.00400/) (2023)
- [Soccer Event Detection Using Deep Learning](https://mlsanity.com/paper/2102.04331/) (2021)
- [Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks](https://mlsanity.com/paper/2106.14344/) (2021)
- [Adversarial Motorial Prototype Framework for Open Set Recognition](https://mlsanity.com/paper/2108.04225/) (2021)
- [Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis](https://mlsanity.com/paper/2307.04541/) (2023)
- [Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition](https://mlsanity.com/paper/2105.13557/) (2021)
- [Open-Set Recognition of Breast Cancer Treatments](https://mlsanity.com/paper/2201.02923/) (2022)
- [Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps](https://mlsanity.com/paper/2202.13642/) (2022)
- [Representation learning with function call graph transformations for malware open set recognition](https://mlsanity.com/paper/2205.06918/) (2022)
- [Semantic Driven Energy based Out-of-Distribution Detection](https://mlsanity.com/paper/2208.10787/) (2022)
- [Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery](https://mlsanity.com/paper/2302.04427/) (2023)
- [Thinkback: Task-SpecificOut-of-Distribution Detection](https://mlsanity.com/paper/2107.06668/) (2021)
- [Fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing](https://mlsanity.com/paper/2204.00597/) (2022)
- [Proxy-Anchor and EVT-Driven Continual Learning Method for Generalized Category Discovery](https://mlsanity.com/paper/2504.08550/) (2025)
- [Malware families discovery via Open-Set Recognition on Android manifest permissions](https://mlsanity.com/paper/2505.12750/) (2025)
- [Hi-OSCAR: Hierarchical Open-set Classifier for Human Activity Recognition](https://mlsanity.com/paper/2510.08635/) (2025)
- [A Novel Contrastive Loss for Zero-Day Network Intrusion Detection](https://mlsanity.com/paper/2601.09902/) (2026)
- [Addressing Randomness in Evaluation Protocols for Out-of-Distribution Detection](https://mlsanity.com/paper/2203.00382/) (2022)
- [Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling](https://mlsanity.com/paper/2207.05957/) (2022)

## Abstract (arXiv)

Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation for addressing the open set recognition problem. In this representation instances from the same class are close to each other while instances from different classes are further apart, resulting in statistically significant improvement when compared to other approaches on three datasets from two different domains.