Awesome Open Set Recognition list

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

• Papers

  • Open Set Recognition
    • Traditional Machine Learning Methods-based
    • Deep Neural Network-based
  • Out-of-Distribution
  • Anomaly Detection
  • Open Set Domain Adaptation
  • Open World Recognition

• License

• Contributing

Tutorials

Tutorial & survey

• Toward Open Set Recognition, Scheirer W J, de Rezende Rocha A, Sapkota A, et al. (PAMI, 2013).

• Towards Open World Recognition, Bendale A, Boult T. (CVPR, 2015).

• Lifelong Machine Learning, Zhiyuan Chen and Bing Liu. (2018).

• Recent Advances in Open Set Recognition: A Survey, Geng C, Huang S, Chen S. (arXiv, 2018).

• Recent Advances in Open Set Recognition: A Survey v2, Chuanxing Geng, Sheng-jun Huang, Songcan Chen. (arXiv, 2019).

Papers

Open Set Recognition

Traditional Machine Learning Methods-based

2013

• Toward Open Set Recognition, Scheirer W J, de Rezende Rocha A, Sapkota A, et al. (PAMI, 2013).[code].

2014

• Probability models for open set recognition, Scheirer W J, Jain L P, Boult T E. (PAMI, 2014). [code].
• Multi-class open set recognition using probability of inclusion, Jain L P, Scheirer W J, Boult T E. (ECCV, 2014). [code].

2016

• Breaking the closed world assumption in text classification, Fei G, Liu B. (NAACL, 2016).

2017

• Sparse representation-based open set recognition, Zhang H, Patel V M. (PAMI, 2017).
• Best fitting hyperplanes for classification, Cevikalp H. (PAMI, 2017). [code].
• Polyhedral conic classifiers for visual object detection and classification, Cevikalp H, Triggs B. Rigling B D. (CVPR, 2017).
• Fast and Accurate Face Recognition with Image Sets, Cevikalp H, Yavuz H S. (ICCVW, 2017). [code]
• Nearest neighbors distance ratio open-set classifier, Júnior P R M, de Souza R M, Werneck R O, et al. (Machine Learning, 2017).

2018

• Data-Fusion Techniques for Open-Set Recognition Problems, Neira M A C, Júnior P R M, Rocha A, et al. (IEEE Access, 2018).
• Towards open-set face recognition using hashing functions, Vareto R, Silva S, Costa F, et al. (IJCB, 2018). [code].
• Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective, Hanze Dong, Yanwei Fu, Leonid Sigal, Sung Ju Hwang, Yu-Gang Jiang, Xiangyang Xue. (arXiv, 2018).

2019

• Specialized Support Vector Machines for Open-set Recognition, Pedro Ribeiro Mendes Júnior, Terrance E. Boult, Jacques Wainer, Anderson Rocha (arXiv, 2019).

Deep Neural Network-based

2015

• A bounded neural network for open set recognition, Cardoso D O, França F, Gama J. (IJCNN, 2015).

2016

• Towards open set deep networks, Bendale A, Boult T E. (CVPR, 2016). [code].

2017

• Weightless neural networks for open set recognition, Cardoso D O, Gama J, França F M G. (Machine Learning, 2017).
• Adversarial Robustness: Softmax versus Openmax, Rozsa A, Günther M, Boult T E. (arXiv, 2017).
• DOC: Deep open classification of text documents, Shu L, Xu H, Liu B. Doc. (arXiv, 2017). [code].
• Generative openmax for multi-class open set classification, Ge Z Y, Demyanov S, Chen Z, et al. (arXiv, 2017).
• Open-category classification by adversarial sample generation, Yu Y, Qu W Y, Li N, et al. (IJCAI, 2017). [code]

2018

• Open category detection with PAC guarantees, Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks. (ICML, 2018). [code].
• Open Set Text Classification using Convolutional Neural Networks, Prakhya S, Venkataram V, Kalita J. (NLPIR, 2018).
• Learning a Neural-network-based Representation for Open Set Recognition, Hassen M, Chan P K. (arXiv, 2018).
• Unseen Class Discovery in Open-world Classification, Shu L, Xu H, Liu B. (arXiv, 2018).
• Reducing Network Agnostophobia, Akshay Raj Dhamija, Manuel Günther, Terrance E. Boult. (NeurIPS 2018). [code].
• Open Set Adversarial Examples, Zhedong Z, Liang Z, Zhilan H, et al. (arXiv, 2018).
• Open Set Learning with Counterfactual Images, Neal L, Olson M, Fern X, et al. (ECCV, 2018). [code]
• The extreme value machine, Rudd E M, Jain L P, Scheirer W J, et al. (PAMI, 2018). [code]
• Extreme Value Theory for Open Set Classification-GPD and GEV Classifiers, Vignotto E, Engelke S. (arXiv, 2018).

2019

• The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning, Benjamin J. Meyer, Tom Drummond. (ICRA, 2019).
• Deep CNN-based Multi-task Learning for Open-Set Recognition, Poojan Oza, Vishal M. Patel. (arXiv, 2019, Under Review).
• Classification-Reconstruction Learning for Open-Set Recognition, 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, Paresh Malalur, Tommi Jaakkola. (arXiv, 2019).
• Open-Set Recognition Using Intra-Class Splitting, Patrick Schlachter, Yiwen Liao, Bin Yang. (EUSIPCO, 2019).
• Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks, Stefano Imoscopi, Volodya Grancharov, Sigurdur Sverrisson, Erlendur Karlsson, Harald Pobloth. (arXiv, 2019).
• Large-Scale Long-Tailed Recognition in an Open World, ZiweiLiu, ZhongqiMiao, XiaohangZhan, et al. (CVPR, Oral, 2019).[code]
• Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?, Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Visvanathan Ramesh. (ICCVW, 2019). [code]
• Deep Transfer Learning for Multiple Class Novelty Detection, Pramuditha Perera, Vishal M. Patel. (CVPR, 2019). [code]
• From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer, Haipeng Xiong, Hao Lu, Chengxin Liu, Liang Liu, Zhiguo Cao, Chunhua Shen. (ICCV, 2019). [code]
• Open-set human activity recognition based on micro-Doppler signatures, Yang Y, Hou C, Lang Y, et al. (Pattern Recognition, 2019).
• C2AE: Class Conditioned Auto-Encoder for Open-set Recognition, Poojan Oza, Vishal M Patel. (CVPR, 2019, oral).

2020

• Conditional Gaussian Distribution Learning for Open Set Recognition, Xin Sun, Zhenning Yang, Chi Zhang, Guohao Peng, Keck-Voon Ling. (CVPR 2020). [code].
• OpenGAN: Open Set Generative Adversarial Networks, Luke Ditria, Benjamin J. Meyer, Tom Drummond. (Submitted to ECCV 2020)
• Collective decision for open set recognition, Chuanxing Geng, Songcan Chen. (IEEE TKDE, 2020).
• One-vs-Rest Network-based Deep Probability Model for Open Set Recognition, Jaeyeon Jang, Chang Ouk Kim. (arXiv 2020)
• Hybrid Models for Open Set Recognition, Hongjie Zhang, Ang Li, Jie Guo, Yanwen Guo. (ECCV 2020).
• Learning Open Set Network with Discriminative Reciprocal Points, Guangyao Chen, Limeng Qiao, Yemin Shi, Peixi Peng, Jia Li, Tiejun Huang, Shiliang Pu, Yonghong Tian. (ECCV 2020)
• Class Anchor Clustering: a Distance-based Loss for Training Open Set Classifiers, Dimity Miller, Niko Sünderhauf, Michael Milford, Feras Dayoub. (ArXiv 2020)
• MMF: A loss extension for feature learning in open set recognition, Jingyun Jia, Philip K. Chan. (ArXiv 2020)
• Fully Convolutional Open Set Segmentation, 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. Yang Yang, Zhen-Qiang Sun, Hui Xiong, Jian Yang. (ArXiv 2020)
• Open Set Recognition with Conditional Probabilistic Generative Models. Xin Sun, Chi Zhang, Guosheng Lin, Keck-Voon Ling. (ArXiv 2020)

Out-of-Distribution

2017

• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. Dan Hendrycks and Kevin Gimpel. (ICLR, 2017). [code].

2018

• Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks. Shiyu Liang, Yixuan Li, R. Srikant. (ICLR, 2018). [code].
• Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin. (ICLR, 2018). [code]
• WAIC, but Why? Generative Ensembles for Robust Anomaly Detection. Hyunsun Choi, Eric Jang, Alexander A. Alemi. (arXiv, 2018).
• A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin. (NeurIPS, 2018). [code]

2019

• Deep Anomaly Detection with Outlier Exposure, Dan Hendrycks, Mantas Mazeika, Thomas Dietterich. (ICLR, 2019). [code]
• Do Deep Generative Models Know What They Don't Know?. Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan. (ICLR, 2019).
• Likelihood Ratios for Out-of-Distribution Detection. Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan. (NeurIPS, 2019). [code]
• Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy. 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. Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf. (CVPR 2019). [code]
• Outlier Exposure with Confidence Control for Out-of-Distribution Detection. Aristotelis-Angelos Papadopoulos, Mohammad Reza Rajati, Nazim Shaikh, Jiamian Wang. (arXiv, 2019). [code]
• Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song. (NeurIPS 2019). [code]

2020

• Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models. 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. Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira. (CVPR 2020)
• Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks. 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. 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]
• The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches. Vahdat Abdelzad, Krzysztof Czarnecki, Rick Salay. (ArXiv 2020)
• Density of States Estimation for Out-of-Distribution Detection. 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. Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Liu, Jasper Snoek, Balaji Lakshminarayanan. (ArXiv 2020)
• Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data. Julian Bitterwolf, Alexander Meinke, Matthias Hein. (ArXiv 2020). [code]
• BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty. 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. 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)

Anomaly Detection

need to survey more..

2018

• Deep Anomaly Detection Using Geometric Transformations. Izhak Golan, Ran El-Yaniv. (NeurIPS, 2018). [code].

2019

• A Benchmark for Anomaly Segmentation. Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song. (arXiv 2019). [code].

2020

• Classification-Based Anomaly Detection for General Data. Liron Bergman, Yedid Hoshen. (ICLR, 2020).

Open Set Domain Adaptation

2017

• Open Set Domain Adaptation, Pau Panareda Busto, Juergen Gall. (ICCV 2017).
• Label Efficient Learning of Transferable Representations across Domains and Tasks, Zelun Luo, Yuliang Zou, Judy Hoffman, Li Fei-Fei. (NeurIPS 2017).

2018

• Open set domain adaptation by backpropagation, Kuniaki Saito, Shohei Yamamoto, Yoshitaka Ushiku, Tatsuya Harada. (ECCV 2018).

2019

• Separate to Adapt: Open Set Domain Adaptation via Progressive Separation. Hong Liu, Zhangjie Cao, Mingsheng Long, Jianmin Wang, Qiang Yang. (CVPR 2019).
• Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization. Junbao Zhuo, Shuhui Wang, Shuhao Cui, Qingming Huang. (CVPR 2019).
• Weakly Supervised Open-Set Domain Adaptation by Dual-Domain Collaboration. Shuhan Tan, Jiening Jiao, Wei-Shi Zheng. (CVPR 2019). [code]
• Learning Factorized Representations for Open-set Domain Adaptation, Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann. (ICLR 2019).
• Known-class Aware Self-ensemble for Open Set Domain Adaptation, Qing Lian, Wen Li, Lin Chen, Lixin Duan. (arXiv 2019).
• Open Set Domain Adaptation: Theoretical Bound and Algorithm, Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang. (arXiv 2019).
• Open Set Domain Adaptation for Image and Action Recognition, Pau Panareda Busto, Ahsan Iqbal, Juergen Gall. (arXiv 2019).
• Attract or Distract: Exploit the Margin of Open Set, Qianyu Feng, Guoliang Kang, Hehe Fan, Yi Yang. (ICCV 2019).

2020

• Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation, Jongbin Ryu, Jiun Bae, Jongwoo Lim. (arXiv 2020).
• Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation, Dongliang Chang, Aneeshan Sain, Zhanyu Ma, Yi-Zhe Song, Jun Guo. (arXiv 2020). [code]
• On the Effectiveness of Image Rotation for Open Set Domain Adaptation, Silvia Bucci, Mohammad Reza Loghmani, Tatiana Tommasi. (ECCV 2020). [code]
• Open Set Domain Adaptation with Multi-Classifier Adversarial Network, Tasfia Shermin, Guojun Lu, Shyh Wei Teng, Manzur Murshed, Ferdous Sohel. (arXiv 2020).
• Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation, Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu. (arXiv 2020).

Open World Recognition

2015

• Towards Open World Recognition, Bendale A, Boult T. (CVPR, 2015).

2016

• Learning Cumulatively to Become More Knowledgeable, Geli Fei, Shuai Wang, Bing Liu. (KDD, 2016).
• Online open world recognition, De Rosa R, Mensink T, Caputo B. (arXiv, 2016).

2017

• Open-World Visual Recognition Using Knowledge Graphs, Lonij V, Rawat A, Nicolae M I. (arXiv, 2017).

2018

• Unseen Class Discovery in Open-world Classification, Shu L, Xu H, Liu B. (arXiv, 2018).
• The extreme value machine, Rudd E M, Jain L P, Scheirer W J, et al. (PAMI, 2018).
• Learning to Accept New Classes without Training, Xu H, Liu B, Shu L, et al. (arXiv, 2018).
• ODN: Opening the Deep Network for Open-Set Action Recognition, Shi Y, Wang Y, Zou Y, et al. (ICME, 2018).

2019

• P-ODN: Prototype based Open Deep Network for Open Set Recognition, Yu Shu, Yemin Shi, Yaowei Wang, Tiejun Huang, Yonghong Tian. (arXiv 2019).
• Learning and the Unknown: Surveying Steps Toward Open World Recognition, 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. Martin Mundt, Sagnik Majumder, Iuliia Pliushch, Visvanathan Ramesh. (arXiv 2019).
• Open-world Learning and Application to Product Classification. Hu Xu, Bing Liu, Lei Shu, P. Yu. (WWW 2019). [code]

License

License

To the extent possible under law, Guangyao Chen has waived all and related or neighboring rights to this work.

Contributing

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Contributions

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