# A Survey on Open Set Recognition

arXiv: https://arxiv.org/abs/2109.00893 · HTML: https://mlsanity.com/paper/2109.00893/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 177 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 30 of 55)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://mlsanity.com/paper/1705.07263/) (2017)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [Detecting Adversarial Samples from Artifacts](https://mlsanity.com/paper/1703.00410/) (2017)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Adversarially Learned One-Class Classifier for Novelty Detection](https://mlsanity.com/paper/1802.09088/) (2018)
- [On the (Statistical) Detection of Adversarial Examples](https://mlsanity.com/paper/1702.06280/) (2017)
- [Reducing Network Agnostophobia](https://mlsanity.com/paper/1811.04110/) (2018)
- [Generative OpenMax for Multi-Class Open Set Classification](https://mlsanity.com/paper/1707.07418/) (2017)
- [Open Set Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1804.10427/) (2018)
- [An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild](https://mlsanity.com/paper/1605.04253/) (2016)
- [Classification-Reconstruction Learning for Open-Set Recognition](https://mlsanity.com/paper/1812.04246/) (2018)
- [C2AE: Class Conditioned Auto-Encoder for Open-set Recognition](https://mlsanity.com/paper/1904.01198/) (2019)
- [Domain Adaptation for Visual Applications: A Comprehensive Survey](https://mlsanity.com/paper/1702.05374/) (2017)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)

## Built on by (most cited first, 25 of 25)

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey](https://mlsanity.com/paper/2407.21794/) (2024)
- [SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery](https://mlsanity.com/paper/2408.14371/) (2024)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Anomaly Detection Based on Isolation Mechanisms: A Survey](https://mlsanity.com/paper/2403.10802/) (2024)
- [Calibrated Out-of-Distribution Detection with a Generic Representation](https://mlsanity.com/paper/2303.13148/) (2023)
- [AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems](https://mlsanity.com/paper/2402.06287/) (2024)
- [Confident AI](https://mlsanity.com/paper/2202.05957/) (2022)
- [Simple Domain Generalization Methods are Strong Baselines for Open Domain Generalization](https://mlsanity.com/paper/2303.18031/) (2023)
- [Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey](https://mlsanity.com/paper/2404.05219/) (2024)
- [Open-Set Multivariate Time-Series Anomaly Detection](https://mlsanity.com/paper/2310.12294/) (2023)
- [Managing the unknown: a survey on Open Set Recognition and tangential areas](https://mlsanity.com/paper/2312.08785/) (2023)
- [Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks](https://mlsanity.com/paper/2409.18235/) (2024)
- [Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection](https://mlsanity.com/paper/2308.11480/) (2023)
- [Unsupervised detection and open-set classification of fast-ramped flexibility activation events](https://mlsanity.com/paper/2111.02174/) (2021)
- [torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python](https://mlsanity.com/paper/2305.09646/) (2023)
- [Comparison of Embedded Spaces for Deep Learning Classification](https://mlsanity.com/paper/2408.01767/) (2024)
- [Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streams](https://mlsanity.com/paper/2411.00876/) (2024)
- [UNSEEN: A Cross-Stack LLM Unlearning Defense against AR-LLM Social Engineering Attacks](https://mlsanity.com/paper/2604.23141/) (2026)
- [Layout-Aware Representation Learning for Open-Set ID Fraud Discovery](https://mlsanity.com/paper/2605.05215/) (2026)
- [Target Domain Data induces Negative Transfer in Mixed Domain Training with Disjoint Classes](https://mlsanity.com/paper/2303.01003/) (2023)
- [Informed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection](https://mlsanity.com/paper/2405.05836/) (2024)
- [Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation](https://mlsanity.com/paper/2505.12803/) (2025)
- [Open-Set Vein Biometric Recognition with Deep Metric Learning](https://mlsanity.com/paper/2604.14874/) (2026)

## Abstract (arXiv)

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned by the models during training. In this paper, we provide a survey of existing works about OSR and distinguish their respective advantages and disadvantages to help out new researchers interested in the subject. The categorization of OSR models is provided along with an extensive summary of recent progress. Additionally, the relationships between OSR and its related tasks including multi-class classification and novelty detection are analyzed. It is concluded that OSR can appropriately deal with unknown instances in the real-world where capturing all possible classes in the training data is not practical. Lastly, applications of OSR are highlighted and some new directions for future research topics are suggested.