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.
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].
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].
Open category detection with PAC guarantees, Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks. (ICML, 2018). [code].
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples, Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin. (ICLR, 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 Anomaly Detection with Outlier Exposure, Dan Hendrycks, Mantas Mazeika, Thomas Dietterich. (ICLR, 2019). [code]
*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).
*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).
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).
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