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.

• Tutorials

• Challenges

• Papers

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

• License

• Contributing

• 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).

• A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges. Salehi M, Mirzaei H, Hendrycks D, Li Y, Rohban MH, Sabokrou M. (arXiv 2021).

• Visual Perception and Learning in an Open World, CVPR 2022.
• Dealing with Novelty in Open Worlds: DNOW, WACV 2022.
• Open World Image Classification Challenge, CVPR 2021.

• Understanding open-set recognition by Jacobian norm and inter-class separation (Arxiv) 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. Salah Abouzaid, Timo Jaeschke, Simon Kueppers, Jan Barowski, Nils Pohl. (IEEE TMTT 2023). [code].
• From anomaly detection to open set recognition: Bridging the gap Hakan Cevikalp, Bedirhan Uzun, Yusuf Salk, Hasan Saribas, Okan Köpüklü. (Pattern Recognition 2023)
• Large-Scale Open-Set Classification Protocols for ImageNet. Andres Palechor, Annesha Bhoumik, Manuel Günther. (WACV 2023) [code]
• The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition. Jun Cen, Di Luan, Shiwei Zhang, Yixuan Pei, Yingya Zhang, Deli Zhao, Shaojie Shen, Qifeng Chen. (ICLR 2022).

• Class-specific semantic reconstruction for open set recognition. Hongzhi Huang, Yu Wang, Qinghua Hu, Ming-Ming Cheng. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2022). [code].
• OpenAUC: Towards AUC-Oriented Open-Set Recognition. 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. Antonio Alliegro, Francesco Cappio Borlino, Tatiana Tommasi. (ArXiv 2022). [code].
• Measuring Human Perception to Improve Handwritten Document Transcription. Jin Huang, Derek Prijatelj, Justin Dulay, Walter Scheirer. (TPAMI 2022)
• Open-Set Semi-Supervised Object Detection. Yen-Cheng Liu, Chih-Yao Ma, Xiaoliang Dai, Junjiao Tian, Peter Vajda, Zijian He, Zsolt Kira. (ECCV 2022). [code].
• DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition. Matej Grcić, Petra Bevandić, Siniša Šegvić. (ECCV 2022). [code].
• Difficulty-Aware Simulator for Open Set Recognition. WonJun Moon, Junho Park, Hyun Seok Seong, Cheol-Ho Cho, Jae-Pil Heo. (ECCV 2022). [code].
• Unseen Classes at a Later Time? No Problem. Hari Chandana Kuchibhotla, Sumitra S Malagi, Shivam Chandhok, Vineeth N Balasubramanian. (CVPR 2022). [code].
• OSSGAN: Open-Set Semi-Supervised Image Generation. Kai Katsumata, Duc Minh Vo, Hideki Nakayama. (CVPR 2022). [code].
• OpenTAL: Towards Open Set Temporal Action Localization. Wentao Bao, Qi Yu, Yu Kong. (CVPR 2022). [code].
• The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods. Thomas G. Dietterich, Alexander Guyer. (ArXiv 2022).
• LUNA: Localizing Unfamiliarity Near Acquaintance for Open-Set Long-Tailed Recognition. Jiarui Cai, Yizhou Wang, Hung-Min Hsu, Jenq-Neng Hwang, Kelsey Magrane, Craig Rose. (AAAI 2022).
• Learngene: From Open-World to Your Learning Task. Qiufeng Wang, Xin Geng, Shuxia Lin, Shiyu Xia, Lei Qi, Ning Xu. (AAAI 2022).
• Learning Network Architecture for Open-Set Recognition. Xuelin Zhang, Xuelian Cheng, Donghao Zhang, Paul Bonnington, Zongyuan Ge. (AAAI 2022).
• PMAL: Open Set Recognition via Robust Prototype Mining. Jing Lu, Yunlu Xu, Hao Li, Zhanzhan Cheng, Yi Niu. (AAAI 2022).
• Open-Set Recognition: A Good Closed-Set Classifier is All You Need. Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman. (ICLR 2022). [code].

• Adversarial Motorial Prototype Framework for Open Set Recognition. Ziheng Xia, Penghui Wang, Ganggang Dong, Hongwei Liu. (ArXiv 2021).
• OpenGAN: Open-Set Recognition via Open Data Generation. Shu Kong, Deva Ramanan. (ICCV 2021). [code].
• Trash To Treasure: Harvesting OOD Data With Cross-Modal Matching for Open-Set Semi-Supervised Learning. 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. 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. Hui Zhang, Henghui Ding. (ICCV 2021)
• Towards Discovery and Attribution of Open-World GAN Generated Images. Sharath Girish, Saksham Suri, Saketh Rambhatla, Abhinav Shrivastava. (ICCV 2021)
• Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation. Weiyao Wang, Matt Feiszli, Heng Wang, Du Tran. (ICCV 2021)
• Deep Metric Learning for Open World Semantic Segmentation. Jun Cen, Peng Yun, Junhao Cai, Michael Yu Wang, Ming Liu. (ICCV 2021)
• NGC: A Unified Framework for Learning With Open-World Noisy Data. Zhi-Fan Wu, Tong Wei, Jianwen Jiang, Chaojie Mao, Mingqian Tang, Yu-Feng Li. (ICCV 2021)
• Large Scale Open-Set Deep Logo Detection. Muhammet Bastan, Hao-Yu Wu, Tian Cao, Bhargava Kota, Mehmet Tek. (ArXiv 2021). [code].
• OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers. Kuniaki Saito, Donghyun Kim, Kate Saenko. (ArXiv 2021). [code].
• Zero-Shot Open Set Detection by Extending CLIP. Sepideh Esmaeilpour, Bing Liu, Eric Robertson, Lei Shu. (ArXiv 2021).
• Adversarial Reciprocal Points Learning for Open Set Recognition. Guangyao Chen, Peixi Peng, Xiangqian Wang, Yonghong Tian. (TPAMI 2021). [code].
• Conditional Variational Capsule Network for Open Set Recognition. Yunrui Guo, Guglielmo Camporese, Wenjing Yang, Alessandro Sperduti, Lamberto Ballan. (ICCV 2021). [code]
• Evidential Deep Learning for Open Set Action Recognition. Wentao Bao, Qi Yu, Yu Kong. (ICCV 2021). [code]
• M2IOSR: Maximal Mutual Information Open Set Recognition. Xin Sun, Henghui Ding, Chi Zhang, Guosheng Lin, Keck-Voon Ling. (ArXiv 2021)
• OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers. Kuniaki Saito, Donghyun Kim, Kate Saenko. (ArXiv 2021)
• Exemplar-Based Open-Set Panoptic Segmentation Network. 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. Lakshman Balasubramanian, Friedrich Kruber, Michael Botsch, Ke Deng. (IEEE Intelligent Vehicles 2021)
• Open-set Face Recognition for Small Galleries Using Siamese Networks. 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. Jingyun Jia, Philip K. Chan. (ArXiv 2021).
• Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition. Jingyun Jia, Philip K. Chan. (ArXiv 2021).
• Counterfactual Zero-Shot and Open-Set Visual Recognition. Zhongqi Yue, Tan Wang, Hanwang Zhang, Qianru Sun, Xian-Sheng Hua. (CVPR 2021). [code].
• Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition. Hakan Cevikalp, Bedirhan Uzun, Okan Köpüklü, Gurkan Ozturk.(ArXiv 2021)
• Learning Placeholders for Open-Set Recognition. Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan.(CVPR 2021)
• Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition. Jaeyeon Jang, Chang Ouk Kim.(ArXiv 2021)

• Conditional Gaussian Distribution Learning for Open Set Recognition, Xin Sun, Zhenning Yang, Chi Zhang, Guohao Peng, Keck-Voon Ling. (CVPR 2020). [code].
• Generative-discriminative Feature Representations for Open-set Recognition, 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, Bo Liu, Hao Kang, Haoxiang Li, Gang Hua, Nuno Vasconcelos. (CVPR 2020)
• OpenGAN: Open Set Generative Adversarial Networks, Luke Ditria, Benjamin J. Meyer, Tom Drummond. (arXiv 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)
• Open-set Adversarial Defense, Rui Shao, Pramuditha Perera, Pong C. Yuen, Vishal M. Patel. (ECCV 2020)
• Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning, Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa. (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)
• Convolutional Prototype Network for Open Set Recognition. 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].

• 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).

• 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).

• 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]

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

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

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

• 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).

• 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).

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

• 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].

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

• Understanding open-set recognition by Jacobian norm and inter-class separation (Arxiv) Jaewoo Park, Hojin Park, Eunju Jeong, Andrew Beng Jin Teoh (Pattern Recognition 2023)

• Learning Bounds for Open-Set Learning. Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang. (ICML 2021). [code].

• Learning Representations that Support Robust Transfer of Predictors. Yilun Xu, Tommi Jaakkola. (ArXiv 2021). [code]

• Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain. Guangyao Chen, Peixi Peng, Li Ma, Jia Li, Lin Du, Yonghong Tian. (ICCV 2021). [code]

• Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation. Robin Chan, Matthias Rottmann, Hanno Gottschalk. (ICCV 2021). [code]

• Online Continual Learning With Natural Distribution Shifts: An Empirical Study With Visual Data. Zhipeng Cai, Ozan Sener, Vladlen Koltun. (ICCV 2021). [code]

• MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction. Patrick Dendorfer, Sven Elflein, Laura Leal-Taixe. (ICCV 2021). [code]

• Semantically Coherent Out-of-Distribution Detection. Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang, Ziwei Liu. (ICCV 2021). [code]

• CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue. 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. Haoyue Bai, Fengwei Zhou, Lanqing Hong, Nanyang Ye, S.-H. Gary Chan, Zhenguo Li. (ICCV 2021)

• CrossNorm and SelfNorm for Generalization under Distribution Shifts. Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris Metaxas. (ICCV 2021). [code]

• Towards a Theoretical Framework of Out-of-Distribution Generalization. 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. Alexander Meinke, Julian Bitterwolf, Matthias Hein. (ArXiv 2021).

• Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure. Jingyang Zhang, Nathan Inkawhich, Yiran Chen, Hai Li. (ArXiv 2021).

• Multi-task Transformation Learning for Robust Out-of-Distribution Detection. Sina Mohseni, Arash Vahdat, Jay Yadawa. (ArXiv 2021).

• OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms. Nanyang Ye, Kaican Li, Lanqing Hong, Haoyue Bai, Yiting Chen, Fengwei Zhou, Zhenguo Li. (ArXiv 2021).

• Exploring the Limits of Out-of-Distribution Detection. Stanislav Fort, Jie Ren, Balaji Lakshminarayanan. (ArXiv 2021).

• Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?. Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville. (ICML 2021).

• Out-of-Distribution Generalization in Kernel Regression. Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan. (ArXiv 2021).

• MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space. Rui Huang, Yixuan Li. (CVPR 2021). [code]

• MOOD: Multi-level Out-of-distribution Detection. Ziqian Lin, Sreya Dutta Roy, Yixuan Li. (CVPR 2021). [code]

• SSD: A Unified Framework for Self-Supervised Outlier Detection, Vikash Sehwag, Mung Chiang, Prateek Mittal. (ICLR 2021). [code]

• Background Data Resampling for Outlier-Aware Classification, Yi Li, Nuno Vasconcelos. (CVPR 2020) [code]
• Robust Out-of-distribution Detection for Neural Networks, Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha. (arXiv 2020) [code]
• 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)
• 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)
• OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification, Taewon Jeong, Heeyoung Kim. (NeurIPS 2020).
• CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances, Jihoon Tack, Sangwoo Mo, Jongheon Jeong, Jinwoo Shin. (NeurIPS 2020). [code].
• Iterative VAE as a predictive brain model for out-of-distribution generalization, Victor Boutin, Aimen Zerroug, Minju Jung, Thomas Serre. (NeurIPS 2020 Workshop SVRHM).
• Certifiably Adversarially Robust Detection of Out-of-Distribution Data, Julian Bitterwolf, Alexander Meinke, Matthias Hein. (NeurIPS 2020). [code].
• Deep Evidential Regression, Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus. (NeurIPS 2020). [code]
• Feature Space Singularity for Out-of-Distribution Detection, Haiwen Huang, Zhihan Li, Lulu Wang, Sishuo Chen, Bin Dong, Xinyu Zhou. (AAAIW 2020). [code]

• 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]
• Evidential Deep Learning to Quantify Classification Uncertainty. Murat Sensoy, Lance Kaplan, Melih Kandemir. (NeurIPS 2019). [code]

• 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]
• Predictive uncertainty estimation via prior networks. Andrey Malinin, Mark Gales. (NeurIPS, 2018).

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

need to survey more..

• Multiresolution Knowledge Distillation for Anomaly Detection. Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H. Rohban, Hamid R. Rabiee. (CVPR 2021). [code]

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

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

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

• Towards Novel Target Discovery Through Open-Set Domain Adaptation. Taotao Jing, Hongfu Liu, Zhengming Ding. (ICCV 2021).

• 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]
• Towards Inheritable Models for Open-Set Domain Adaptation, Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur, Rahul M V, R. Venkatesh Babu. (CVPR 2020)
• Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation, Yingwei Pan, Ting Yao, Yehao Li, Chong-Wah Ngo, Tao Mei. (CVPR 2020)
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