ZhangJUJU / 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

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
    • Adversarial Learning-based
    • EVT-based
  • Out-of-Distribution
  • 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).

• 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].

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).

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).
• C2AE: Class Conditioned Auto-Encoder for Open-set Recognition, Poojan Oza, Vishal M Patel. (CVPR, 2019, oral).
• *Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks, Stefano Imoscopi, Volodya Grancharov, Sigurdur Sverrisson, Erlendur Karlsson, Harald Pobloth. (arXiv, 2019).
• Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization, Junbao Zhuo, Shuhui Wang, Shuhao Cui, Qingming Huang (CVPR, 2019).
• Large-Scale Long-Tailed Recognition in an Open World, ZiweiLiu, ZhongqiMiao, XiaohangZhan, et al. (CVPR, Oral, 2019).[code]
• Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition, Martin Mundt, Sagnik Majumder, Iuliia Pliushch, Visvanathan Ramesh. (arXiv, 2019).
• 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).

Adversarial Learning-based

2017

• *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 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]

2019

• Open-set human activity recognition based on micro-Doppler signatures, Yang Y, Hou C, Lang Y, et al. (Pattern Recognition, 2019).

Extreme Value Theory-based

2018

• 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

• Collective decision for open set recognition, Chuanxing Geng, Songcan Chen. (arXiv, 2019).

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]

2019

• Deep Anomaly Detection with Outlier Exposure, Dan Hendrycks, Mantas Mazeika, Thomas Dietterich. (ICLR, 2019). [code]
• 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)

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).

Open World Recognition

2015

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

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).

License

License

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

Contributing

Please see CONTRIBUTING for details.

Contributions

Contributions are most welcome, if you have any suggestions and improvements, please create an issue or raise a pull request.