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https://github.com/iCGY96/awesome_OpenSetRecognition_list
A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
https://github.com/iCGY96/awesome_OpenSetRecognition_list
List: awesome_OpenSetRecognition_list
awesome-list open-set open-set-domain-adaptation open-set-recognition open-source open-word-recognition out-of-distribution-detection
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A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
- Host: GitHub
- URL: https://github.com/iCGY96/awesome_OpenSetRecognition_list
- Owner: iCGY96
- Created: 2019-01-16T03:13:07.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-03-01T20:20:42.000Z (7 months ago)
- Last Synced: 2024-05-19T18:54:58.215Z (4 months ago)
- Topics: awesome-list, open-set, open-set-domain-adaptation, open-set-recognition, open-source, open-word-recognition, out-of-distribution-detection
- Homepage:
- Size: 368 KB
- Stars: 1,017
- Watchers: 37
- Forks: 145
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
Awesome Lists containing this project
- awesomeai - Open Set Recognition
- awesome-trustworthy-deep-learning - Awesome Open Set Recognition list - commit/iCGY96/awesome_OpenSetRecognition_list) (Other Lists)
- awesome-ai-awesomeness - Open Set Recognition
- awesome-ai-list-guide - awesome_OpenSetRecognition_list - of-distribution, open set domain adaptation and open world recognition (Speech)
- awesome-lists-machine-learning - Open Set Recognition
- ultimate-awesome - awesome_OpenSetRecognition_list - A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition. (Other Lists / PowerShell Lists)
README
# Awesome Open Set Recognition list
[](https://github.com/sindresorhus/awesome)
[](https://github.com/iCGY96/awesome_OpenSetRecognition_list/graphs/commit-activity)
[](https://github.com/iCGY96/awesome_OpenSetRecognition_list/stargazers)
[](https://github.com/iCGY96/awesome_OpenSetRecognition_list)
[](https://github.com/iCGY96/awesome_OpenSetRecognition_list/watchers)
[](https://github.com/iCGY96/awesome_OpenSetRecognition_list/network/members)
[](https://github.com/iCGY96/awesome_OpenSetRecognition_list/network/members)
> A curated list of papers & ressources linked to open set recognition, out-of-distribution, open set domain adaptation, and open world recognition
**Note that:**
- **This list is not exhaustive.**
- **Tables use alphabetical order for fairness.**
## Contents
- [Tutorials](#tutorials)
- [Challenges](#challenges)
- [Papers](#papers)
- [Open Set Recognition](#papers-osr)
- [Deep Neural Network-based](#papers-osr-dnn)
- [Traditional Machine Learning Methods-based](#papers-osr-ml)
- [Open Set Learning Theory](#papers-oslt)
- [Out-of-Distribution](#papers-odd)
- [Anomaly Detection](#papers-ad)
- [Open Set Domain Adaptation](#papers-osda)
- [Open World Recognition](#papers-owr)
- [License](#license)
- [Contributing](#contributing)
## Tutorial & survey
- [Toward Open Set Recognition](https://ieeexplore.ieee.org/abstract/document/6365193), Scheirer W J, de Rezende Rocha A, Sapkota A, et al. (**PAMI, 2013**).
- [Towards Open World Recognition](https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Bendale_Towards_Open_World_2015_CVPR_paper.html), Bendale A, Boult T. (**CVPR, 2015**).
- [Lifelong Machine Learning](https://www.cs.uic.edu/~liub/lifelong-machine-learning.html), Zhiyuan Chen and Bing Liu. (**2018**).
- [Recent Advances in Open Set Recognition: A Survey](https://arxiv.org/abs/1811.08581), Geng C, Huang S, Chen S. (**arXiv, 2018**).
- [Recent Advances in Open Set Recognition: A Survey v2](https://arxiv.org/abs/1811.08581v2), Chuanxing Geng, Sheng-jun Huang, Songcan Chen. (**arXiv, 2019**).
- [A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges](https://arxiv.org/abs/2110.14051). Salehi M, Mirzaei H, Hendrycks D, Li Y, Rohban MH, Sabokrou M. (**arXiv 2021**).
## Open World Vision
- [Visual Perception and Learning in an Open World](http://www.cs.cmu.edu/~shuk/vplow.html), CVPR 2022.
- [Dealing with Novelty in Open Worlds: DNOW](https://www.cs.umd.edu/~pulkit/DNOW_workshop/), WACV 2022.
- [Open World Image Classification Challenge](https://eval.ai/web/challenges/challenge-page/1041/overview), CVPR 2021.
# Papers
### Deep Neural Network-based
##### 2023
- [Understanding open-set recognition by Jacobian norm and inter-class separation](https://www.sciencedirect.com/science/article/abs/pii/S0031320323006404) ([Arxiv](https://arxiv.org/abs/2209.11436)) Jaewoo Park, Hojin Park, Eunju Jeong, Andrew Beng Jin Teoh (**Pattern Recognition 2023**)
- [Deep Learning-Based Material Characterization Using FMCW Radar With Open-Set Recognition Technique](https://doi.org/10.1109/TMTT.2023.3276053). Salah Abouzaid, Timo Jaeschke, Simon Kueppers, Jan Barowski, Nils Pohl. (**IEEE TMTT 2023**). [[code]](https://github.com/Salah-Abouzaid/MaterialCharML.git).
- [From anomaly detection to open set recognition: Bridging the gap](https://www.sciencedirect.com/science/article/pii/S0031320323000869) Hakan Cevikalp, Bedirhan Uzun, Yusuf Salk, Hasan Saribas, Okan Köpüklü. (**Pattern Recognition 2023**)
- [Large-Scale Open-Set Classification Protocols for ImageNet](https://openaccess.thecvf.com/content/WACV2023/html/Palechor_Large-Scale_Open-Set_Classification_Protocols_for_ImageNet_WACV_2023_paper.html). Andres Palechor, Annesha Bhoumik, Manuel Günther. (**WACV 2023**) [[code]](https://github.com/AIML-IfI/openset-imagenet)
- [The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition](https://arxiv.org/abs/2302.04002). Jun Cen, Di Luan, Shiwei Zhang, Yixuan Pei, Yingya Zhang, Deli Zhao, Shaojie Shen, Qifeng Chen. (**ICLR 2022**).
##### 2022
- [Class-specific semantic reconstruction for open set recognition](https://arxiv.org/pdf/2207.02158.pdf). Hongzhi Huang, Yu Wang, Qinghua Hu, Ming-Ming Cheng. IEEE Transactions on Pattern Analysis and Machine Intelligence (**TPAMI 2022**). [[code]](https://github.com/xyzedd/CSSR).
- [OpenAUC: Towards AUC-Oriented Open-Set Recognition](https://arxiv.org/abs/2210.13458). Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang. (**NeurIPS 2022**).
- [Towards Open Set 3D Learning: A Benchmark on Object Point Clouds](https://arxiv.org/abs/2207.11554). Antonio Alliegro, Francesco Cappio Borlino, Tatiana Tommasi. (**ArXiv 2022**). [[code]](https://github.com/antoalli/3d_os).
- [Measuring Human Perception to Improve Handwritten Document Transcription](https://arxiv.org/abs/2209.03519). Jin Huang, Derek Prijatelj, Justin Dulay, Walter Scheirer. (**TPAMI 2022**)
- [Open-Set Semi-Supervised Object Detection](https://ycliu93.github.io/projects/ossod.html). Yen-Cheng Liu, Chih-Yao Ma, Xiaoliang Dai, Junjiao Tian, Peter Vajda, Zijian He, Zsolt Kira. (**ECCV 2022**). [[code]](https://github.com/facebookresearch/OSSOD).
- [DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition](https://arxiv.org/abs/2207.02606). Matej Grcić, Petra Bevandić, Siniša Šegvić. (**ECCV 2022**). [[code]](https://github.com/matejgrcic/DenseHybrid).
- [Difficulty-Aware Simulator for Open Set Recognition](https://arxiv.org/abs/2207.10024). WonJun Moon, Junho Park, Hyun Seok Seong, Cheol-Ho Cho, Jae-Pil Heo. (**ECCV 2022**). [[code]](https://github.com/wjun0830/Difficulty-Aware-Simulator).
- [Unseen Classes at a Later Time? No Problem](https://arxiv.org/abs/2203.16517). Hari Chandana Kuchibhotla, Sumitra S Malagi, Shivam Chandhok, Vineeth N Balasubramanian. (**CVPR 2022**). [[code]](https://github.com/sumitramalagi/Unseen-classes-at-a-later-time).
- [OSSGAN: Open-Set Semi-Supervised Image Generation](https://arxiv.org/abs/2204.14249). Kai Katsumata, Duc Minh Vo, Hideki Nakayama. (**CVPR 2022**). [[code]](https://github.com/raven38/ossgan).
- [OpenTAL: Towards Open Set Temporal Action Localization](https://arxiv.org/pdf/2203.05114.pdf). Wentao Bao, Qi Yu, Yu Kong. (**CVPR 2022**). [[code]](https://github.com/Cogito2012/OpenTAL).
- [The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods](https://arxiv.org/abs/2203.02486). Thomas G. Dietterich, Alexander Guyer. (**ArXiv 2022**).
- [LUNA: Localizing Unfamiliarity Near Acquaintance for Open-Set Long-Tailed Recognition](https://aaai-2022.virtualchair.net/poster_aaai10200). Jiarui Cai, Yizhou Wang, Hung-Min Hsu, Jenq-Neng Hwang, Kelsey Magrane, Craig Rose. (**AAAI 2022**).
- [Learngene: From Open-World to Your Learning Task](https://aaai-2022.virtualchair.net/poster_aaai3013). Qiufeng Wang, Xin Geng, Shuxia Lin, Shiyu Xia, Lei Qi, Ning Xu. (**AAAI 2022**).
- [Learning Network Architecture for Open-Set Recognition](https://aaai-2022.virtualchair.net/poster_aaai12763). Xuelin Zhang, Xuelian Cheng, Donghao Zhang, Paul Bonnington, Zongyuan Ge. (**AAAI 2022**).
- [PMAL: Open Set Recognition via Robust Prototype Mining](https://aaai-2022.virtualchair.net/poster_aaai284). Jing Lu, Yunlu Xu, Hao Li, Zhanzhan Cheng, Yi Niu. (**AAAI 2022**).
- [Open-Set Recognition: A Good Closed-Set Classifier is All You Need](https://arxiv.org/abs/2110.06207). Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman. (**ICLR 2022**). [[code]](https://github.com/sgvaze/osr_closed_set_all_you_need).
##### 2021
- [Adversarial Motorial Prototype Framework for Open Set Recognition](https://arxiv.org/abs/2108.04225). Ziheng Xia, Penghui Wang, Ganggang Dong, Hongwei Liu. (**ArXiv 2021**).
- [OpenGAN: Open-Set Recognition via Open Data Generation](https://arxiv.org/abs/2104.02939v2). Shu Kong, Deva Ramanan. (**ICCV 2021**). [[code]](https://github.com/aimerykong/OpenGAN).
- [Trash To Treasure: Harvesting OOD Data With Cross-Modal Matching for Open-Set Semi-Supervised Learning](https://arxiv.org/abs/2108.05617). Junkai Huang, Chaowei Fang, Weikai Chen, Zhenhua Chai, Xiaolin Wei, Pengxu Wei, Liang Lin, Guanbin Li. (**ICCV 2021**)
- [Energy-Based Open-World Uncertainty Modeling for Confidence Calibration](https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Energy-Based_Open-World_Uncertainty_Modeling_for_Confidence_Calibration_ICCV_2021_paper.html). Yezhen Wang, Bo Li, Tong Che, Kaiyang Zhou, Ziwei Liu, Dongsheng Li. (**ICCV 2021**)
- [Prototypical Matching and Open Set Rejection for Zero-Shot Semantic Segmentation](https://openaccess.thecvf.com/content/ICCV2021/html/Zhang_Prototypical_Matching_and_Open_Set_Rejection_for_Zero-Shot_Semantic_Segmentation_ICCV_2021_paper.html). Hui Zhang, Henghui Ding. (**ICCV 2021**)
- [Towards Discovery and Attribution of Open-World GAN Generated Images](https://arxiv.org/abs/2105.04580). Sharath Girish, Saksham Suri, Saketh Rambhatla, Abhinav Shrivastava. (**ICCV 2021**)
- [Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation](https://arxiv.org/abs/2104.04691). Weiyao Wang, Matt Feiszli, Heng Wang, Du Tran. (**ICCV 2021**)
- [Deep Metric Learning for Open World Semantic Segmentation](https://arxiv.org/abs/2108.04562). Jun Cen, Peng Yun, Junhao Cai, Michael Yu Wang, Ming Liu. (**ICCV 2021**)
- [NGC: A Unified Framework for Learning With Open-World Noisy Data](https://arxiv.org/abs/2108.11035). Zhi-Fan Wu, Tong Wei, Jianwen Jiang, Chaojie Mao, Mingqian Tang, Yu-Feng Li. (**ICCV 2021**)
- [Large Scale Open-Set Deep Logo Detection](https://arxiv.org/abs/1911.07440v2). Muhammet Bastan, Hao-Yu Wu, Tian Cao, Bhargava Kota, Mehmet Tek. (**ArXiv 2021**). [[code]](https://github.com/mubastan/osld).
- [OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers](https://arxiv.org/abs/2105.14148v2). Kuniaki Saito, Donghyun Kim, Kate Saenko. (**ArXiv 2021**). [[code]](https://github.com/VisionLearningGroup/OP_Match).
- [Zero-Shot Open Set Detection by Extending CLIP](https://arxiv.org/abs/2109.02748v2). Sepideh Esmaeilpour, Bing Liu, Eric Robertson, Lei Shu. (**ArXiv 2021**).
- [Adversarial Reciprocal Points Learning for Open Set Recognition](https://arxiv.org/abs/2103.00953). Guangyao Chen, Peixi Peng, Xiangqian Wang, Yonghong Tian. (**TPAMI 2021**). [[code]](https://github.com/iCGY96/ARPL).
- [Conditional Variational Capsule Network for Open Set Recognition](https://arxiv.org/abs/2104.09159). Yunrui Guo, Guglielmo Camporese, Wenjing Yang, Alessandro Sperduti, Lamberto Ballan. (**ICCV 2021**). [[code]](https://github.com/guglielmocamporese/cvaecaposr)
- [Evidential Deep Learning for Open Set Action Recognition](https://arxiv.org/abs/2107.10161v1). Wentao Bao, Qi Yu, Yu Kong. (**ICCV 2021**). [[code]]( https://github.com/Cogito2012/DEAR)
- [M2IOSR: Maximal Mutual Information Open Set Recognition](https://arxiv.org/abs/2108.02373v2). Xin Sun, Henghui Ding, Chi Zhang, Guosheng Lin, Keck-Voon Ling. (**ArXiv 2021**)
- [OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers](https://arxiv.org/abs/2105.14148v1). Kuniaki Saito, Donghyun Kim, Kate Saenko. (**ArXiv 2021**)
- [Exemplar-Based Open-Set Panoptic Segmentation Network](https://arxiv.org/abs/2105.08336v2). Jaedong Hwang, Seoung Wug Oh, Joon-Young Lee, Bohyung Han. (**CVPR 2021**)
- [Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios](https://arxiv.org/abs/2105.07635v1). Lakshman Balasubramanian, Friedrich Kruber, Michael Botsch, Ke Deng. (**IEEE Intelligent Vehicles 2021**)
- [Open-set Face Recognition for Small Galleries Using Siamese Networks](https://arxiv.org/abs/2105.06967v1). Gabriel Salomon, Alceu Britto, Rafael H. Vareto, William R. Schwartz, David Menotti. (**ArXiv 2021**)
- [MMF: A loss extension for feature learning in open set recognition](https://arxiv.org/abs/2006.15117v2). Jingyun Jia, Philip K. Chan. (**ArXiv 2021**).
- [Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition](https://arxiv.org/abs/2105.13557v1). Jingyun Jia, Philip K. Chan. (**ArXiv 2021**).
- [Counterfactual Zero-Shot and Open-Set Visual Recognition](https://arxiv.org/abs/2103.00887). Zhongqi Yue, Tan Wang, Hanwang Zhang, Qianru Sun, Xian-Sheng Hua. (**CVPR 2021**). [[code]](https://github.com/yue-zhongqi/gcm-cf).
- [Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition](https://arxiv.org/abs/2102.12570v1). Hakan Cevikalp, Bedirhan Uzun, Okan Köpüklü, Gurkan Ozturk.(**ArXiv 2021**)
- [Learning Placeholders for Open-Set Recognition](https://arxiv.org/abs/2103.15086). Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan.(**CVPR 2021**)
- [Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition](http://arxiv.org/abs/2103.12871v1). Jaeyeon Jang, Chang Ouk Kim.(**ArXiv 2021**)
##### 2020
- [Conditional Gaussian Distribution Learning for Open Set Recognition](https://arxiv.org/abs/2003.08823v2), Xin Sun, Zhenning Yang, Chi Zhang, Guohao Peng, Keck-Voon Ling. (**CVPR 2020**). [[code]](https://github.com/BraveGump/CGDL-for-Open-Set-Recognition).
- [Generative-discriminative Feature Representations for Open-set Recognition](https://openaccess.thecvf.com/content_CVPR_2020/papers/Perera_Generative-Discriminative_Feature_Representations_for_Open-Set_Recognition_CVPR_2020_paper.pdf), Pramuditha Perera, Vlad I. Morariu, Rajiv Jain, Varun Manjunatha, Curtis Wigington, Vicente Ordonez, Vishal M. Patel. (**CVPR 2020**).
- [Few-Shot Open-Set Recognition Using Meta-Learning](https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Few-Shot_Open-Set_Recognition_Using_Meta-Learning_CVPR_2020_paper.html), Bo Liu, Hao Kang, Haoxiang Li, Gang Hua, Nuno Vasconcelos. (**CVPR 2020**)
- [OpenGAN: Open Set Generative Adversarial Networks](https://arxiv.org/abs/2003.08074v1), Luke Ditria, Benjamin J. Meyer, Tom Drummond. (**arXiv 2020**)
- [Collective decision for open set recognition](https://arxiv.org/abs/1806.11258v4), Chuanxing Geng, Songcan Chen. (**IEEE TKDE, 2020**).
- [One-vs-Rest Network-based Deep Probability Model for Open Set Recognition](https://arxiv.org/abs/2004.08067), Jaeyeon Jang, Chang Ouk Kim. (**arXiv 2020**)
- [Hybrid Models for Open Set Recognition](https://arxiv.org/abs/2003.12506v1), Hongjie Zhang, Ang Li, Jie Guo, Yanwen Guo. (**ECCV 2020**).
- [Learning Open Set Network with Discriminative Reciprocal Points](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480511.pdf), Guangyao Chen, Limeng Qiao, Yemin Shi, Peixi Peng, Jia Li, Tiejun Huang, Shiliang Pu, Yonghong Tian. (**ECCV 2020**)
- [Open-set Adversarial Defense](https://arxiv.org/abs/2009.00814v1), Rui Shao, Pramuditha Perera, Pong C. Yuen, Vishal M. Patel. (**ECCV 2020**)
- [Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning](https://arxiv.org/abs/2007.11330v1), Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa. (**ECCV 2020**)
- [Class Anchor Clustering: a Distance-based Loss for Training Open Set Classifiers](https://arxiv.org/abs/2004.02434v2), Dimity Miller, Niko Sünderhauf, Michael Milford, Feras Dayoub. (**ArXiv 2020**)
- [MMF: A loss extension for feature learning in open set recognition](https://arxiv.org/abs/2006.15117v1), Jingyun Jia, Philip K. Chan. (**ArXiv 2020**)
- [Fully Convolutional Open Set Segmentation](https://arxiv.org/abs/2006.14673v1), Hugo Oliveira, Caio Silva, Gabriel L. S. Machado, Keiller Nogueira, Jefersson A. dos Santos. (**ArXiv 2020**)
- [S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification](https://arxiv.org/abs/2008.04662v1). Yang Yang, Zhen-Qiang Sun, Hui Xiong, Jian Yang. (**ArXiv 2020**)
- [Open Set Recognition with Conditional Probabilistic Generative Models](https://arxiv.org/abs/2008.05129v1). Xin Sun, Chi Zhang, Guosheng Lin, Keck-Voon Ling. (**ArXiv 2020**)
- [Convolutional Prototype Network for Open Set Recognition](https://ieeexplore.ieee.org/document/9296325). Hong-Ming Yang, Xu-Yao Zhang, Fei Yin, Qing Yang, Cheng-Lin Liu. (**PAMI 2020**)
- [The Overlooked Elephant of Object Detection: Open Set](). Akshay Raj Dhamija, Manuel Günther, Jonathan Ventura, Terrance E. Boult. (**WACV 2020**) [[code]](https://github.com/Vastlab/Elephant-of-object-detection).
##### 2019
- [The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning](https://arxiv.org/abs/1902.10363), Benjamin J. Meyer, Tom Drummond. (**ICRA, 2019**).
- [Deep CNN-based Multi-task Learning for Open-Set Recognition](https://arxiv.org/abs/1903.03161), Poojan Oza, Vishal M. Patel. (**arXiv, 2019, Under Review**).
- [Classification-Reconstruction Learning for Open-Set Recognition](https://arxiv.org/abs/1812.04246), Ryota Yoshihashi, Wen Shao, Rei Kawakami, Shaodi You, Makoto Iida, Takeshi Naemura. (**CVPR, 2019**).
- [Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition](https://arxiv.org/abs/1903.06538), Paresh Malalur, Tommi Jaakkola. (**arXiv, 2019**).
- [Open-Set Recognition Using Intra-Class Splitting](https://arxiv.org/abs/1903.04774), Patrick Schlachter, Yiwen Liao, Bin Yang. (**EUSIPCO, 2019**).
- [Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks](https://arxiv.org/abs/1904.01269v1), Stefano Imoscopi, Volodya Grancharov, Sigurdur Sverrisson, Erlendur Karlsson, Harald Pobloth. (**arXiv, 2019**).
- [Large-Scale Long-Tailed Recognition in an Open World](https://arxiv.org/abs/1904.05160v1), ZiweiLiu, ZhongqiMiao, XiaohangZhan, et al. (**CVPR, Oral, 2019**).[[code]](https://github.com/zhmiao/OpenLongTailRecognition-OLTR)
- [Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?](https://arxiv.org/abs/1908.09625v1), Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Visvanathan Ramesh. (**ICCVW, 2019**). [**[code]**](https://github.com/MrtnMndt/Deep_Openset_Recognition_through_Uncertainty)
- [Deep Transfer Learning for Multiple Class Novelty Detection](https://arxiv.org/abs/1903.02196), Pramuditha Perera, Vishal M. Patel. (**CVPR, 2019**). [[code]](https://github.com/PramuPerera/TransferLearningNovelty)
- [From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer](https://arxiv.org/abs/1908.06473), Haipeng Xiong, Hao Lu, Chengxin Liu, Liang Liu, Zhiguo Cao, Chunhua Shen. (**ICCV, 2019**). [[code]](https://github.com/xhp-hust-2018-2011/S-DCNet)
- [Open-set human activity recognition based on micro-Doppler signatures](https://www.sciencedirect.com/science/article/pii/S003132031830270X), Yang Y, Hou C, Lang Y, et al. (**Pattern Recognition, 2019**).
- [C2AE: Class Conditioned Auto-Encoder for Open-set Recognition](https://arxiv.org/abs/1904.01198v1), Poojan Oza, Vishal M Patel. (**CVPR, 2019, oral**).
##### 2018
- [Open category detection with PAC guarantees](http://proceedings.mlr.press/v80/liu18e/liu18e.pdf), Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks. (**ICML, 2018**). [**[code]**](https://github.com/liusi2019/ocd).
- [Open Set Text Classification using Convolutional Neural Networks](http://www.cs.uccs.edu/~jkalita/papers/2017/SridhamaPrakhyaICON2017.pdf), Prakhya S, Venkataram V, Kalita J. (**NLPIR, 2018**).
- [Learning a Neural-network-based Representation for Open Set Recognition](https://arxiv.org/abs/1802.04365), Hassen M, Chan P K. (**arXiv, 2018**).
- [Unseen Class Discovery in Open-world Classification](https://arxiv.org/abs/1802.04365), Shu L, Xu H, Liu B. (**arXiv, 2018**).
- [Reducing Network Agnostophobia](https://papers.nips.cc/paper/8129-reducing-network-agnostophobia), Akshay Raj Dhamija, Manuel Günther, Terrance E. Boult. (**NeurIPS 2018**). [**[code]**](https://github.com/Vastlab/Reducing-Network-Agnostophobia).
- [Open Set Adversarial Examples](https://arxiv.org/abs/1809.02681), Zhedong Z, Liang Z, Zhilan H, et al. (**arXiv, 2018**).
- [Open Set Learning with Counterfactual Images](https://link.springer.com/chapter/10.1007/978-3-030-01231-1_38), Neal L, Olson M, Fern X, et al. (**ECCV, 2018**). [**[code]**](https://github.com/lwneal/counterfactual-open-set)
- [The extreme value machine](https://ieeexplore.ieee.org/abstract/document/7932895/), Rudd E M, Jain L P, Scheirer W J, et al. (**PAMI, 2018**). [**[code]**](https://github.com/EMRResearch/ExtremeValueMachine)
- [Extreme Value Theory for Open Set Classification-GPD and GEV Classifiers](https://arxiv.org/abs/1808.09902), Vignotto E, Engelke S. (**arXiv, 2018**).
##### 2017
- [Weightless neural networks for open set recognition](https://link.springer.com/article/10.1007/s10994-017-5646-4), Cardoso D O, Gama J, França F M G. (**Machine Learning, 2017**).
- [Adversarial Robustness: Softmax versus Openmax](https://arxiv.org/abs/1708.01697), Rozsa A, Günther M, Boult T E. (**arXiv, 2017**).
- [DOC: Deep open classification of text documents](https://arxiv.org/abs/1709.08716), Shu L, Xu H, Liu B. Doc. (**arXiv, 2017**). [**[code]**](https://github.com/alexander-rakhlin/CNN-for-Sentence-Classification-in-Keras).
- [Generative openmax for multi-class open set classification](https://arxiv.org/abs/1707.07418), Ge Z Y, Demyanov S, Chen Z, et al. (**arXiv, 2017**).
- [Open-category classification by adversarial sample generation](https://arxiv.org/abs/1705.08722), Yu Y, Qu W Y, Li N, et al. (**IJCAI, 2017**). [**[code]**](https://github.com/eyounx/ASG)
##### 2016
- [Towards open set deep networks](https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Bendale_Towards_Open_Set_CVPR_2016_paper.html), Bendale A, Boult T E. (**CVPR, 2016**). [**[code]**](https://github.com/abhijitbendale/OSDN).
##### 2015
- [A bounded neural network for open set recognition](https://ieeexplore.ieee.org/abstract/document/7280680/), Cardoso D O, França F, Gama J. (**IJCNN, 2015**).
### Traditional Machine Learning Methods-based
##### 2019
- [Specialized Support Vector Machines for Open-set Recognition](https://arxiv.org/abs/1606.03802v8), Pedro Ribeiro Mendes Júnior, Terrance E. Boult, Jacques Wainer, Anderson Rocha (**arXiv, 2019**).
##### 2018
- [Data-Fusion Techniques for Open-Set Recognition Problems](https://ieeexplore.ieee.org/abstract/document/8332940/), Neira M A C, Júnior P R M, Rocha A, et al. (**IEEE Access, 2018**).
- [Towards open-set face recognition using hashing functions](https://ieeexplore.ieee.org/abstract/document/8272751/), Vareto R, Silva S, Costa F, et al. (**IJCB, 2018**). [**[code]**](https://github.com/rafaelvareto/HPLS-HFCN-openset).
- [Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective](https://arxiv.org/abs/1810.07368), Hanze Dong, Yanwei Fu, Leonid Sigal, Sung Ju Hwang, Yu-Gang Jiang, Xiangyang Xue. (**arXiv, 2018**).
##### 2017
- [Sparse representation-based open set recognition](https://ieeexplore.ieee.org/abstract/document/7577876/), Zhang H, Patel V M. (**PAMI, 2017**).
- [Best fitting hyperplanes for classification](https://ieeexplore.ieee.org/abstract/document/7506010/), Cevikalp H. (**PAMI, 2017**). [**[code]**](http://mlcv.ogu.edu.tr/softwarefh.html).
- [Polyhedral conic classifiers for visual object detection and classification](http://openaccess.thecvf.com/content_cvpr_2017/papers/Cevikalp_Polyhedral_Conic_Classifiers_CVPR_2017_paper.pdf), Cevikalp H, Triggs B. Rigling B D. (**CVPR, 2017**).
- [Fast and Accurate Face Recognition with Image Sets](http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w23/Cevikalp_Fast_and_Accurate_ICCV_2017_paper.pdf), Cevikalp H, Yavuz H S. (**ICCVW, 2017**). [**[code]**](https://github.com/hezhangsprinter/SROSR)
- [Nearest neighbors distance ratio open-set classifier](https://link.springer.com/article/10.1007/s10994-016-5610-8), Júnior P R M, de Souza R M, Werneck R O, et al. (**Machine Learning, 2017**).
##### 2016
- [Breaking the closed world assumption in text classification](http://www.aclweb.org/anthology/N16-1061), Fei G, Liu B. (**NAACL, 2016**).
##### 2014
- [Probability models for open set recognition](https://ieeexplore.ieee.org/abstract/document/6809169/), Scheirer W J, Jain L P, Boult T E. (**PAMI, 2014**). [**[code]**](https://github.com/ljain2/libsvm-openset).
- [Multi-class open set recognition using probability of inclusion](https://link.springer.com/chapter/10.1007/978-3-319-10578-9_26), Jain L P, Scheirer W J, Boult T E. (**ECCV, 2014**). [**[code]**](https://github.com/ljain2/libsvm-openset).
##### 2013
- [Toward Open Set Recognition](https://ieeexplore.ieee.org/abstract/document/6365193), Scheirer W J, de Rezende Rocha A, Sapkota A, et al. (**PAMI, 2013**).[**[code]**](https://github.com/ljain2/libsvm-openset).
## Open Set Learning Theory
##### 2023
- [Understanding open-set recognition by Jacobian norm and inter-class separation](https://www.sciencedirect.com/science/article/abs/pii/S0031320323006404) ([Arxiv](https://arxiv.org/abs/2209.11436)) Jaewoo Park, Hojin Park, Eunju Jeong, Andrew Beng Jin Teoh (**Pattern Recognition 2023**)
##### 2021
- [Learning Bounds for Open-Set Learning](https://arxiv.org/abs/2106.15792). Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang. (**ICML 2021**). [[code]](https://github.com/Anjin-Liu/Openset_Learning_AOSR).
##### 2021
- [Learning Representations that Support Robust Transfer of Predictors](https://arxiv.org/abs/2110.09940). Yilun Xu, Tommi Jaakkola. (**ArXiv 2021**). [**[code]**](https://github.com/Newbeeer/TRM)
- [Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain](https://arxiv.org/abs/2108.08487). Guangyao Chen, Peixi Peng, Li Ma, Jia Li, Lin Du, Yonghong Tian. (**ICCV 2021**). [**[code]**](https://github.com/iCGY96/APR)
- [Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation](https://openaccess.thecvf.com/content/ICCV2021/html/Chan_Entropy_Maximization_and_Meta_Classification_for_Out-of-Distribution_Detection_in_Semantic_ICCV_2021_paper.html). Robin Chan, Matthias Rottmann, Hanno Gottschalk. (**ICCV 2021**). [**[code]**](https://github.com/robin-chan/meta-ood)
- [Online Continual Learning With Natural Distribution Shifts: An Empirical Study With Visual Data](https://arxiv.org/abs/2108.09020). Zhipeng Cai, Ozan Sener, Vladlen Koltun. (**ICCV 2021**). [**[code]**](https://github.com/intellabs/continuallearning)
- [MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction](https://openaccess.thecvf.com/content/ICCV2021/html/Dendorfer_MG-GAN_A_Multi-Generator_Model_Preventing_Out-of-Distribution_Samples_in_Pedestrian_Trajectory_ICCV_2021_paper.html). Patrick Dendorfer, Sven Elflein, Laura Leal-Taixe. (**ICCV 2021**). [**[code]**](https://github.com/selflein/MG-GAN)
- [Semantically Coherent Out-of-Distribution Detection](https://arxiv.org/abs/2108.11941). Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang, Ziwei Liu. (**ICCV 2021**). [**[code]**](https://github.com/Jingkang50/ICCV21_SCOOD)
- [CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue](https://arxiv.org/abs/2108.06024). Keke Tang, Dingruibo Miao, Weilong Peng, Jianpeng Wu, Yawen Shi, Zhaoquan Gu, Zhihong Tian, Wenping Wang. (**ICCV 2021**)
- [NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization](https://openaccess.thecvf.com/content/ICCV2021/html/Bai_NAS-OoD_Neural_Architecture_Search_for_Out-of-Distribution_Generalization_ICCV_2021_paper.html). Haoyue Bai, Fengwei Zhou, Lanqing Hong, Nanyang Ye, S.-H. Gary Chan, Zhenguo Li. (**ICCV 2021**)
- [CrossNorm and SelfNorm for Generalization under Distribution Shifts](https://arxiv.org/abs/2102.02811). Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris Metaxas. (**ICCV 2021**). [**[code]**](https://github.com/amazon-research/crossnorm-selfnorm)
- [Towards a Theoretical Framework of Out-of-Distribution Generalization](https://arxiv.org/abs/2106.04496v1). Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, Liwei Wang. (**ArXiv 2021**).
- [Provably Robust Detection of Out-of-distribution Data (almost) for free](https://arxiv.org/abs/2106.04260v1). Alexander Meinke, Julian Bitterwolf, Matthias Hein. (**ArXiv 2021**).
- [Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure](https://arxiv.org/abs/2106.03917v1). Jingyang Zhang, Nathan Inkawhich, Yiran Chen, Hai Li. (**ArXiv 2021**).
- [Multi-task Transformation Learning for Robust Out-of-Distribution Detection](https://arxiv.org/abs/2106.03899v1). Sina Mohseni, Arash Vahdat, Jay Yadawa. (**ArXiv 2021**).
- [OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms](https://arxiv.org/abs/2106.03721v1). Nanyang Ye, Kaican Li, Lanqing Hong, Haoyue Bai, Yiting Chen, Fengwei Zhou, Zhenguo Li. (**ArXiv 2021**).
- [Exploring the Limits of Out-of-Distribution Detection](https://arxiv.org/abs/2106.03004v1). Stanislav Fort, Jie Ren, Balaji Lakshminarayanan. (**ArXiv 2021**).
- [Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?](https://arxiv.org/abs/2106.02890v1). Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville. (**ICML 2021**).
- [Out-of-Distribution Generalization in Kernel Regression](https://arxiv.org/abs/2106.02261v1). Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan. (**ArXiv 2021**).
- [MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space](https://arxiv.org/abs/2105.01879v1). Rui Huang, Yixuan Li. (**CVPR 2021**). [**[code]**](https://github.com/deeplearning-wisc/large_scale_ood)
- [MOOD: Multi-level Out-of-distribution Detection](https://arxiv.org/abs/2104.14726v1). Ziqian Lin, Sreya Dutta Roy, Yixuan Li. (**CVPR 2021**). [**[code]**](https://github.com/deeplearning-wisc/MOOD)
- [SSD: A Unified Framework for Self-Supervised Outlier Detection](https://openreview.net/forum?id=v5gjXpmR8J), Vikash Sehwag, Mung Chiang, Prateek Mittal. (**ICLR 2021**). [**[code]**](https://github.com/inspire-group/SSD)
##### 2020
- [Background Data Resampling for Outlier-Aware Classification](https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Background_Data_Resampling_for_Outlier-Aware_Classification_CVPR_2020_paper.html), Yi Li, Nuno Vasconcelos. (**CVPR 2020**) [[code]](https://github.com/JerryYLi/bg-resample-ood)
- [Robust Out-of-distribution Detection for Neural Networks](https://arxiv.org/abs/2003.09711), Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha. (**arXiv 2020**) [[code]](https://github.com/jfc43/robust-ood-detection)
- [Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models](https://openreview.net/forum?id=SyxIWpVYvr). Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, Jordi Luque. (**ICLR, 2020**)
- [Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data](https://arxiv.org/abs/2002.11297). Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira. (**CVPR 2020**)
- [Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks](https://arxiv.org/abs/2007.03212v1). Doyup Lee, Yeongjae Cheon. (**ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning**)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://arxiv.org/abs/2006.16241v1). Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer. (**ArXiv 2020**). [**[code]**](https://github.com/hendrycks/imagenet-r)
- [The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches](https://arxiv.org/abs/2006.14584v1). Vahdat Abdelzad, Krzysztof Czarnecki, Rick Salay. (**ArXiv 2020**)
- [Density of States Estimation for Out-of-Distribution Detection](https://arxiv.org/abs/2006.09273v2). Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon. (**ArXiv 2020**)
- [Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks](https://arxiv.org/abs/2007.05134v1). Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Liu, Jasper Snoek, Balaji Lakshminarayanan. (**ArXiv 2020**)
- [BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty](https://arxiv.org/abs/2007.06096v1). Théo Guénais, Dimitris Vamvourellis, Yaniv Yacoby, Finale Doshi-Velez, Weiwei Pan. (**ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning**)
- [Contrastive Training for Improved Out-of-Distribution Detection](https://arxiv.org/abs/2007.05566v1). Jim Winkens, Rudy Bunel, Abhijit Guha Roy, Robert Stanforth, Vivek Natarajan, Joseph R. Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Simon Kohl, Taylan Cemgil, S. M. Ali Eslami, Olaf Ronneberger. (**ArXiv 2020**)
- [OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification](https://proceedings.neurips.cc/paper/2020/file/28e209b61a52482a0ae1cb9f5959c792-Paper.pdf), Taewon Jeong, Heeyoung Kim. (**NeurIPS 2020**).
- [CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances](https://arxiv.org/abs/2007.08176), Jihoon Tack, Sangwoo Mo, Jongheon Jeong, Jinwoo Shin. (**NeurIPS 2020**). [**[code]**](https://github.com/alinlab/CSI).
- [Iterative VAE as a predictive brain model for out-of-distribution generalization](https://openreview.net/forum?id=jE6SlVTOFPV), Victor Boutin, Aimen Zerroug, Minju Jung, Thomas Serre. (**NeurIPS 2020 Workshop SVRHM**).
- [Certifiably Adversarially Robust Detection of Out-of-Distribution Data](https://arxiv.org/abs/2007.08473v2), Julian Bitterwolf, Alexander Meinke, Matthias Hein. (**NeurIPS 2020**). [**[code]**](https://gitlab.com/Bitterwolf/GOOD).
- [Deep Evidential Regression](https://arxiv.org/abs/1910.02600), Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus. (**NeurIPS 2020**). [**[code]**](https://github.com/aamini/evidential-deep-learning)
- [Feature Space Singularity for Out-of-Distribution Detection](https://arxiv.org/abs/2011.14654v2), Haiwen Huang, Zhihan Li, Lulu Wang, Sishuo Chen, Bin Dong, Xinyu Zhou. (**AAAIW 2020**). [**[code]**](https://github.com/megvii-research/FSSD_OoD_Detection)
##### 2019
- [Deep Anomaly Detection with Outlier Exposure](https://arxiv.org/abs/1812.04606), Dan Hendrycks, Mantas Mazeika, Thomas Dietterich. (**ICLR, 2019**). [**[code]**](https://github.com/hendrycks/outlier-exposure)
- [Do Deep Generative Models Know What They Don't Know?](https://arxiv.org/abs/1810.09136). Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan. (**ICLR, 2019**).
- [Likelihood Ratios for Out-of-Distribution Detection](https://arxiv.org/abs/1906.02845). Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan. (**NeurIPS, 2019**). [**[code]**](https://github.com/google-research/google-research/tree/master/genomics_ood)
- [Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy](http://openaccess.thecvf.com/content_ICCV_2019/papers/Yu_Unsupervised_Out-of-Distribution_Detection_by_Maximum_Classifier_Discrepancy_ICCV_2019_paper.pdf). Qing Yu, Kiyoharu Aizawa. (**ICCV, 2019**)
- [Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem](https://arxiv.org/abs/1812.05720). Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf. (**CVPR 2019**). [**[code]**](https://github.com/max-andr/relu_networks_overconfident)
- [Outlier Exposure with Confidence Control for Out-of-Distribution Detection](https://arxiv.org/abs/1906.03509). Aristotelis-Angelos Papadopoulos, Mohammad Reza Rajati, Nazim Shaikh, Jiamian Wang. (**arXiv, 2019**). [**[code]**](https://github.com/nazim1021/OOD-detection-using-OECC)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://arxiv.org/abs/1906.12340). Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song. (**NeurIPS 2019**). [**[code]**](https://github.com/hendrycks/ss-ood)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://papers.nips.cc/paper/2018/hash/a981f2b708044d6fb4a71a1463242520-Abstract.html). Murat Sensoy, Lance Kaplan, Melih Kandemir. (**NeurIPS 2019**). [**[code]**](https://github.com/dougbrion/pytorch-classification-uncertainty)
##### 2018
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://arxiv.org/abs/1706.02690). Shiyu Liang, Yixuan Li, R. Srikant. (**ICLR, 2018**). [**[code]**](https://github.com/facebookresearch/odin).
- [Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples](https://arxiv.org/abs/1711.09325). Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin. (**ICLR, 2018**). [**[code]**](https://github.com/alinlab/Confident_classifier)
- [WAIC, but Why? Generative Ensembles for Robust Anomaly Detection](https://arxiv.org/abs/1810.01392). Hyunsun Choi, Eric Jang, Alexander A. Alemi. (**arXiv, 2018**).
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://arxiv.org/abs/1807.03888). Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin. (**NeurIPS, 2018**). [**[code]**](https://github.com/pokaxpoka/deep_Mahalanobis_detector)
- [Predictive uncertainty estimation via prior networks](https://papers.nips.cc/paper/2018/hash/3ea2db50e62ceefceaf70a9d9a56a6f4-Abstract.html). Andrey Malinin, Mark Gales. (**NeurIPS, 2018**).
##### 2017
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://arxiv.org/abs/1610.02136). Dan Hendrycks and Kevin Gimpel. (**ICLR, 2017**). [**[code]**](https://github.com/hendrycks/error-detection).
## Anomaly Detection
[need to survey more..](https://github.com/hoya012/awesome-anomaly-detection)
##### 2021
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html). Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H. Rohban, Hamid R. Rabiee. (**CVPR 2021**). [**[code]**](https://github.com/rohban-lab/Knowledge_Distillation_AD)
##### 2020
- [Classification-Based Anomaly Detection for General Data](https://openreview.net/forum?id=H1lK_lBtvS). Liron Bergman, Yedid Hoshen. (**ICLR, 2020**).
##### 2019
- [A Benchmark for Anomaly Segmentation](https://arxiv.org/abs/1911.11132). Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song. (**arXiv 2019**). [**[code]**](https://github.com/hendrycks/anomaly-seg).
##### 2018
- [Deep Anomaly Detection Using Geometric Transformations](https://arxiv.org/abs/1805.10917). Izhak Golan, Ran El-Yaniv. (**NeurIPS, 2018**). [**[code]**](https://github.com/izikgo/AnomalyDetectionTransformations).
##### 2021
- [Towards Novel Target Discovery Through Open-Set Domain Adaptation](https://arxiv.org/abs/2105.02432). Taotao Jing, Hongfu Liu, Zhengming Ding. (**ICCV 2021**).
##### 2020
- [Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation](https://arxiv.org/abs/2002.03642v1), Jongbin Ryu, Jiun Bae, Jongwoo Lim. (**arXiv 2020**).
- [Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation](https://arxiv.org/abs/2003.03787v2), Dongliang Chang, Aneeshan Sain, Zhanyu Ma, Yi-Zhe Song, Jun Guo. (**arXiv 2020**). [**[code]**](https://github.com/dongliangchang/Mutual-to-Separate/)
- [Towards Inheritable Models for Open-Set Domain Adaptation](https://openaccess.thecvf.com/content_CVPR_2020/html/Kundu_Towards_Inheritable_Models_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.html), Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur, Rahul M V, R. Venkatesh Babu. (**CVPR 2020**)
- [Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation](https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Exploring_Category-Agnostic_Clusters_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.html), Yingwei Pan, Ting Yao, Yehao Li, Chong-Wah Ngo, Tao Mei. (**CVPR 2020**)
- [On the Effectiveness of Image Rotation for Open Set Domain Adaptation](https://arxiv.org/abs/2007.12360v1), Silvia Bucci, Mohammad Reza Loghmani, Tatiana Tommasi. (**ECCV 2020**). [**[code]**](https://github.com/silvia1993/ROS)
- [Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation](https://arxiv.org/abs/2006.13022v1), Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu. (**arXiv 2020**).
- [Progressive Graph Learning for Open-Set Domain Adaptation](https://arxiv.org/abs/2006.12087), Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh. (**ICML 2020**).
- [Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation](https://arxiv.org/abs/2007.00384), Tasfia Shermin, Guojun Lu, Shyh Wei Teng, Manzur Murshed, Ferdous Sohel. (**IEEE TMM 2020**). [**[code]**](https://github.com/tasfia/DAMC)
##### 2019
- [Separate to Adapt: Open Set Domain Adaptation via Progressive Separation](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Separate_to_Adapt_Open_Set_Domain_Adaptation_via_Progressive_Separation_CVPR_2019_paper.pdf). Hong Liu, Zhangjie Cao, Mingsheng Long, Jianmin Wang, Qiang Yang. (**CVPR 2019**).
- [Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhuo_Unsupervised_Open_Domain_Recognition_by_Semantic_Discrepancy_Minimization_CVPR_2019_paper.pdf). Junbao Zhuo, Shuhui Wang, Shuhao Cui, Qingming Huang. (**CVPR 2019**).
- [Weakly Supervised Open-Set Domain Adaptation by Dual-Domain Collaboration](http://openaccess.thecvf.com/content_CVPR_2019/html/Tan_Weakly_Supervised_Open-Set_Domain_Adaptation_by_Dual-Domain_Collaboration_CVPR_2019_paper.html). Shuhan Tan, Jiening Jiao, Wei-Shi Zheng. (**CVPR 2019**). [**[code]**](https://github.com/junbaoZHUO/UODTN)
- [Learning Factorized Representations for Open-set Domain Adaptation](https://openreview.net/forum?id=SJe3HiC5KX), Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann. (**ICLR 2019**).
- [Known-class Aware Self-ensemble for Open Set Domain Adaptation](https://arxiv.org/abs/1905.01068v1), Qing Lian, Wen Li, Lin Chen, Lixin Duan. (**arXiv 2019**).
- [Open Set Domain Adaptation: Theoretical Bound and Algorithm](https://arxiv.org/abs/1907.08375v1), Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang. (**arXiv 2019**).
- [Open Set Domain Adaptation for Image and Action Recognition](https://arxiv.org/abs/1907.12865v1), Pau Panareda Busto, Ahsan Iqbal, Juergen Gall. (**arXiv 2019**).
- [Attract or Distract: Exploit the Margin of Open Set](https://arxiv.org/abs/1908.01925v2), Qianyu Feng, Guoliang Kang, Hehe Fan, Yi Yang. (**ICCV 2019**).
##### 2018
- [Open set domain adaptation by backpropagation](https://arxiv.org/abs/1804.10427), Kuniaki Saito, Shohei Yamamoto, Yoshitaka Ushiku, Tatsuya Harada. (**ECCV 2018**).
##### 2017
- [Open Set Domain Adaptation](http://openaccess.thecvf.com/content_ICCV_2017/papers/Busto_Open_Set_Domain_ICCV_2017_paper.pdf), Pau Panareda Busto, Juergen Gall. (**ICCV 2017**).
- [Label Efficient Learning of Transferable Representations across Domains and Tasks](https://arxiv.org/abs/1712.00123), Zelun Luo, Yuliang Zou, Judy Hoffman, Li Fei-Fei. (**NeurIPS 2017**).
##### 2022
- [Opening Up Open-World Tracking](https://arxiv.org/abs/2104.11221), Yang Liu, Idil Esen Zulfikar, Jonathon Luiten, Achal Dave, Deva Ramanan, Bastian Leibe, Aljoša Ošep, Laura Leal-Taixé. (**CVPR 2022**). [**[code]**](https://github.com/JonathonLuiten/TrackEval/blob/master/docs/OpenWorldTracking-Official/Readme.md)
- [OW-DETR: Open-world Detection Transformer](http://arxiv.org/abs/2112.01513), Akshita Gupta, Sanath Narayan, K J Joseph, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah. (**CVPR 2022**). [**[code]**](https://github.com/akshitac8/OW-DETR)
##### 2021
- [A Unified Objective for Novel Class Discovery](https://arxiv.org/abs/2108.08536), Enrico Fini, Enver Sangineto, Stéphane Lathuilière, Zhun Zhong, Moin Nabi, Elisa Ricci. (**ICCV 2021**). [**[code]**](https://github.com/DonkeyShot21/UNO)
- [Neighborhood Contrastive Learning for Novel Class Discovery](https://arxiv.org/abs/2106.10731), Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, Nicu Sebe. (**CVPR 2021**). [**[code]**](https://github.com/zhunzhong07/NCL)
- [OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World](https://arxiv.org/abs/2004.05551), Zhun Zhong, Linchao Zhu, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe. (**CVPR 2021**).
- [Towards Open World Object Detection](https://arxiv.org/abs/2103.02603), K J Joseph, Salman Khan, Fahad Shahbaz Khan, Vineeth N Balasubramanian. (**CVPR 2021**). [**[code]**](https://github.com/JosephKJ/OWOD)
##### 2020
- [Learning to discover novel visual categories via deep transfer clustering](https://openaccess.thecvf.com/content_ICCV_2019/papers/Han_Learning_to_Discover_Novel_Visual_Categories_via_Deep_Transfer_Clustering_ICCV_2019_paper.pdf), Kai Han, Andrea Vedaldi, Andrew Zisserman. (**ICCV 2019**).
- [Automatically discovering and learning new visual categories with ranking statistics](https://openreview.net/forum?id=BJl2_nVFPB), Han, K., Rebuffi, S.A., Ehrhardt, S., Vedaldi, A., Zisserman. (**ICLR 2020**).
##### 2019
- [P-ODN: Prototype based Open Deep Network for Open Set Recognition](https://arxiv.org/abs/1905.01851v1), Yu Shu, Yemin Shi, Yaowei Wang, Tiejun Huang, Yonghong Tian. (**arXiv 2019**).
- [Learning and the Unknown: Surveying Steps Toward Open World Recognition](https://www.vast.uccs.edu/~tboult/PAPERS/Learning_and_the_Unknown_Surveying_Steps_Toward_Open_World_Recognition_AAAI19.pdf), Terrance Boult, Steve Cruz, Akshay Dhamija, Manuel Günther, James Henrydoss,
Walter J. Scheirer. (**AAAI, 2019**).
- [Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition](https://arxiv.org/abs/1905.12019v2). Martin Mundt, Sagnik Majumder, Iuliia Pliushch, Visvanathan Ramesh. (**arXiv 2019**).
- [Open-world Learning and Application to Product Classification](https://arxiv.org/abs/1809.06004). Hu Xu, Bing Liu, Lei Shu, P. Yu. (**WWW 2019**). [**[code]**](https://github.com/howardhsu/Meta-Open-World-Learning)
##### 2018
- [Unseen Class Discovery in Open-world Classification](https://arxiv.org/abs/1801.05609), Shu L, Xu H, Liu B. (**arXiv, 2018**).
- [The extreme value machine](https://ieeexplore.ieee.org/abstract/document/7932895/), Rudd E M, Jain L P, Scheirer W J, et al. (**PAMI, 2018**).
- [Learning to Accept New Classes without Training](https://arxiv.org/abs/1801.05609), Xu H, Liu B, Shu L, et al. (**arXiv, 2018**).
- [ODN: Opening the Deep Network for Open-Set Action Recognition](https://ieeexplore.ieee.org/abstract/document/8486601/), Shi Y, Wang Y, Zou Y, et al. (**ICME, 2018**).
##### 2017
- [Open-World Visual Recognition Using Knowledge Graphs](https://arxiv.org/abs/1708.08310), Lonij V, Rawat A, Nicolae M I. (**arXiv, 2017**).
##### 2016
- [Learning Cumulatively to Become More Knowledgeable](https://dl.acm.org/citation.cfm?id=2939835), Geli Fei, Shuai Wang, Bing Liu. (**KDD, 2016**).
- [Online open world recognition](https://arxiv.org/abs/1604.02275), De Rosa R, Mensink T, Caputo B. (**arXiv, 2016**).
##### 2015
- [Towards Open World Recognition](https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Bendale_Towards_Open_World_2015_CVPR_paper.html), Bendale A, Boult T. (**CVPR, 2015**).
# License
License []()
To the extent possible under law, [Guangyao Chen](https://github.com/iCGY96/) has waived all and related or neighboring rights to this work.
# Contributing
Please see [CONTRIBUTING](https://github.com/iCGY96/awesome_OpenSet_list/blob/master/contributing.md) for details.
# Contributions
Contributions are most welcome, if you have any suggestions and improvements, please create an issue or raise a pull request.
## Stargazers over time
[](https://starchart.cc/iCGY96/awesome_OpenSetRecognition_list)