A curated list of papers & ressources linked to open set recognition and open world recognition

Note that:

• This list is not exhaustive.
• Tables use alphabetical order for fairness.

• Tutorials

• Papers

  • Open-Set-Recognition
    • Traditional Machine Learning Methods-based
    • Deep Neural Network-based
    • Adversarial Learning-based
    • EVT-based
  • Out-of-Distribution
  • Open-World-Recognition

• OpenSource software resources

  • Open-Set-Recognition
  • 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).

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

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

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].

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

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).

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).

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

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

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].

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).

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. (arXiv, 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).

*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]

*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]

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

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).

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

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

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

*Online open world recognition, De Rosa R, Mensink T, Caputo B. (arXiv, 2016).

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

*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).

Large-Scale Long-Tailed Recognition in an Open World, ZiweiLiu, ZhongqiMiao, XiaohangZhan, et al. (CVPR, Oral, 2019).[code]

P-ODN: Prototype based Open Deep Network for Open Set Recognition, Yu Shu, Yemin Shi, Yaowei Wang, Tiejun Huang, Yonghong Tian. (arXiv 2019).

License

To the extent possible under law, Guangyao Chen has waived all https://arxiv.org/abs/1904.05160v1 and related or neighboring rights to this work.

Please see CONTRIBUTING for details.