14.2.10 Open Set Recongnition

Zhang, H.[He], Patel, V.M.[Vishal M.],
Sparse Representation-Based Open Set Recognition,
PAMI(39), No. 8, August 2017, pp. 1690-1696.
IEEE DOI 1707
Not all classes presented during testing are known during training. Animals, Data models, Image reconstruction, Indexes, Pattern analysis, Testing, Training, Open set recognition, extreme value theory, sparse, representation-based, classification BibRef

de Oliveira Werneck, R.[Rafael], Raveaux, R.[Romain], Tabbone, S.[Salvatore], da Silva Torres, R.[Ricardo],
Learning cost function for graph classification with open-set methods,
PRL(128), 2019, pp. 8-15.
Elsevier DOI 1912
Graph matching, Cost learning, SVM, Open-set recognition BibRef
Earlier:
Learning Cost Functions for Graph Matching,
SSSPR18(345-354).
Springer DOI 1810
BibRef

Dang, S.A.[Sih-Ang], Cao, Z.J.[Zong-Jie], Cui, Z.Y.[Zong-Yong], Pi, Y.M.[Yi-Ming], Liu, N.Y.[Neng-Yuan],
Open Set Incremental Learning for Automatic Target Recognition,
GeoRS(57), No. 7, July 2019, pp. 4445-4456.
IEEE DOI 1907
Classifier with rejection option. Training, Target recognition, Learning systems, Computational modeling, Support vector machines, open set recognition (OSR) BibRef

Geng, C.X.[Chuan-Xing], Tao, L.[Lue], Chen, S.C.[Song-Can],
Guided CNN for generalized zero-shot and open-set recognition using visual and semantic prototypes,
PR(102), 2020, pp. 107263.
Elsevier DOI 2003
Convolutional prototype learning, Generalized zero-shot Learning, Open set recognition BibRef

Othman, E., Bazi, Y.[Yakoub], Melgani, F., Alhichri, H.[Haikel], Alajlan, N.[Naif], Zuair, M.,
Domain Adaptation Network for Cross-Scene Classification,
GeoRS(55), No. 8, August 2017, pp. 4441-4456.
IEEE DOI 1708
Computer architecture, Earth, Feature extraction, Feeds, Machine learning, Neural networks, Remote sensing, Cross-scene classification, distribution mismatch, domain adaptation, multisensor data, pretrained, convolutional, neural, network, (CNN) BibRef

Adayel, R.[Reham], Bazi, Y.[Yakoub], Alhichri, H.[Haikel], Alajlan, N.[Naif],
Deep Open-Set Domain Adaptation for Cross-Scene Classification based on Adversarial Learning and Pareto Ranking,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Loghmani, M.R.[Mohammad Reza], Vincze, M.[Markus], Tommasi, T.[Tatiana],
Positive-unlabeled learning for open set domain adaptation,
PRL(136), 2020, pp. 198-204.
Elsevier DOI 2008
Deep learning, Image classification, Domain adaptation, Open set recognition, Positive-Unlabelled learning BibRef

Fu, Y., Wang, X., Dong, H., Jiang, Y.G., Wang, M., Xue, X., Sigal, L.,
Vocabulary-Informed Zero-Shot and Open-Set Learning,
PAMI(42), No. 12, December 2020, pp. 3136-3152.
IEEE DOI 2011
Semantics, Vocabulary, Training data, Prototypes, Image recognition, Visualization, Learning systems, Vocabulary-informed learning, zero-shot learning BibRef

Liu, S.J.[Sheng-Jie], Shi, Q.[Qian], Zhang, L.P.[Liang-Pei],
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning,
GeoRS(59), No. 6, June 2021, pp. 5085-5102.
IEEE DOI 2106
Hyperspectral imaging, Image recognition, Image reconstruction, Machine learning, Training, Data models, open-set recognition BibRef

Cevikalp, H.[Hakan], Uzun, B.[Bedirhan], Köpüklü, O.[Okan], Ozturk, G.[Gurkan],
Deep compact polyhedral conic classifier for open and closed set recognition,
PR(119), 2021, pp. 108080.
Elsevier DOI 2106
Polyhedral conic classifier, Deep learning, Open set recognition, Image classification, Anomaly detection BibRef

Zhou, H.H.[Hao-Hong], Azzam, M.[Mohamed], Zhong, J.[Jian], Liu, C.[Cheng], Wu, S.[Si], Wong, H.S.[Hau-San],
Knowledge Exchange Between Domain-Adversarial and Private Networks Improves Open Set Image Classification,
IP(30), 2021, pp. 5807-5818.
IEEE DOI 2106
Adaptation models, Training, Task analysis, Knowledge engineering, Computational modeling, Data models, Benchmark testing, image classification BibRef

Feng, Z.[Zeyu], Xu, C.[Chang], Tao, D.C.[Da-Cheng],
Open-Set Hypothesis Transfer With Semantic Consistency,
IP(30), 2021, pp. 6473-6484.
IEEE DOI 2107
Adaptation models, Data models, Predictive models, Semantics, Training, Standards, Entropy, Open-set, domain adaptation, consistency regularization BibRef

Shermin, T.[Tasfia], Lu, G.J.[Guo-Jun], Teng, S.W.[Shyh Wei], Murshed, M.[Manzur], Sohel, F.[Ferdous],
Adversarial Network With Multiple Classifiers for Open Set Domain Adaptation,
MultMed(23), 2021, pp. 2732-2744.
IEEE DOI 2109
Adaptation models, Training, Loss measurement, Generators, Computational modeling, Data models, Task analysis, multi-classifier based weighting module BibRef

Geng, C.X.[Chuan-Xing], Huang, S.J.[Sheng-Jun], Chen, S.C.[Song-Can],
Recent Advances in Open Set Recognition: A Survey,
PAMI(43), No. 10, October 2021, pp. 3614-3631.
IEEE DOI 2109
Survey, Open Set Recognition. Training, Testing, Task analysis, Semantics, Face recognition, Data visualization, Open set recognition/classification, one-shot learning BibRef

Park, J.[Jaewoo], Low, C.Y.[Cheng Yaw], Teoh, A.B.J.[Andrew Beng Jin],
Divergent Angular Representation for Open Set Image Recognition,
IP(31), 2022, pp. 176-189.
IEEE DOI 2112
Data models, Training, Prototypes, Semantics, Loss measurement, Image recognition, Robustness, Open set recognition, representation learning BibRef

Chambers, L.[Lorraine], Gaber, M.M.[Mohamed Medhat],
DeepStreamOS: Fast open-Set classification for convolutional neural networks,
PRL(154), 2022, pp. 75-82.
Elsevier DOI 2202
Open-Set classification, Out-of-Distribution, Deep neural networks, Convolutional neural networks, Streaming machine learning BibRef

Yang, H.M.[Hong-Ming], Zhang, X.Y.[Xu-Yao], Yin, F.[Fei], Yang, Q.[Qing], Liu, C.L.[Cheng-Lin],
Convolutional Prototype Network for Open Set Recognition,
PAMI(44), No. 5, May 2022, pp. 2358-2370.
IEEE DOI 2204
Prototypes, Training, Feature extraction, Robustness, Task analysis, Biological neural networks, Brain modeling, Open-set recognition, generative model BibRef

Zhang, Y.[Yabin], Deng, B.[Bin], Tang, H.[Hui], Zhang, L.[Lei], Jia, K.[Kui],
Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice,
PAMI(44), No. 5, May 2022, pp. 2775-2792.
IEEE DOI 2204
Training, Training data, Task analysis, Testing, Machine learning, Adaptation models, Standards, Domain adaptation, partial or open set domain adaptation BibRef

Yuan, Y.[Yuan], He, X.X.[Xin-Xing], Jiang, Z.[Zhiyu],
Adaptive open domain recognition by coarse-to-fine prototype-based network,
PR(128), 2022, pp. 108657.
Elsevier DOI 2205
Open domain recognition, Image classification, Adaptive openness, Prototype learning, Unknown class recognition BibRef

Guo, Y.[Yunrui], Camporese, G.[Guglielmo], Yang, W.J.[Wen-Jing], Sperduti, A.[Alessandro], Ballan, L.[Lamberto],
Conditional Variational Capsule Network for Open Set Recognition,
ICCV21(103-111)
IEEE DOI 2203
Training, Image recognition, Computational modeling, Neurons, Probabilistic logic, Feature extraction, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Farber, M.[Miriam], Goldenberg, R.[Roman], Leifman, G.[George], Novich, G.[Gal],
Novel Ensemble Diversification Methods for Open-Set Scenarios,
WACV22(3361-3370)
IEEE DOI 2202
Training, Correlation, Image recognition, Computational modeling, Diversity methods, Feature extraction, Biometrics -> Face Processing BibRef

Baktashmotlagh, M.[Mahsa], Chen, T.[Tianle], Salzmann, M.[Mathieu],
Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift,
WACV22(3737-3746)
IEEE DOI 2202
Adaptation models, Training data, Benchmark testing, Data models, Task analysis, Object Detection/Recognition/Categorization BibRef

Pal, D.[Debabrata], Bundele, V.[Valay], Sharma, R.[Renuka], Banerjee, B.[Biplab], Jeppu, Y.[Yogananda],
Few-Shot Open-Set Recognition of Hyperspectral Images with Outlier Calibration Network,
WACV22(2091-2100)
IEEE DOI 2202
Training, Representation learning, Image recognition, Manuals, Semi- and Un- supervised Learning BibRef

Lee, S.[Sanghyuk], Lee, S.H.[Seung-Hyun], Song, B.C.[Byung Cheol],
Contextual Gradient Scaling for Few-Shot Learning,
WACV22(3503-3512)
IEEE DOI 2202
Degradation, Adaptation models, Computational modeling, Classification algorithms, Task analysis, Deep Learning Deep Learning -> Efficient Training and Inference Methods for Networks BibRef

Lee, J.[Jinsol], AlRegib, G.[Ghassan],
Open-Set Recognition With Gradient-Based Representations,
ICIP21(469-473)
IEEE DOI 2201
Training, Image recognition, Neural networks, Detectors, Predictive models, Task analysis, gradients, open-set recognition, out-of-distribution BibRef

Zhou, D.W.[Da-Wei], Ye, H.J.[Han-Jia], Zhan, D.C.[De-Chuan],
Learning Placeholders for Open-Set Recognition,
CVPR21(4399-4408)
IEEE DOI 2111
Training, Manifolds, Face recognition, MIMICs, Transforms, Predictive models BibRef

Mancini, M.[Massimiliano], Naeem, M.F.[Muhammad Ferjad], Xian, Y.Q.[Yong-Qin], Akata, Z.[Zeynep],
Open World Compositional Zero-Shot Learning,
CVPR21(5218-5226)
IEEE DOI 2111
Training, Degradation, Visualization, Computational modeling, Knowledge based systems, Benchmark testing BibRef

Zhong, Z.[Zhun], Zhu, L.C.[Lin-Chao], Luo, Z.M.[Zhi-Ming], Li, S.Z.[Shao-Zi], Yang, Y.[Yi], Sebe, N.[Nicu],
OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in an Open World,
CVPR21(9457-9465)
IEEE DOI 2111
Training, Visualization, Computational modeling, Benchmark testing, Data models, Pattern recognition BibRef

Shu, Y.[Yang], Cao, Z.J.[Zhang-Jie], Wang, C.[Chenyu], Wang, J.[Jianmin], Long, M.S.[Ming-Sheng],
Open Domain Generalization with Domain-Augmented Meta-Learning,
CVPR21(9619-9628)
IEEE DOI 2111
Computational modeling, Data models, Pattern recognition, Microstrip, Task analysis BibRef

Jeong, M.[Minki], Choi, S.[Seokeon], Kim, C.[Changick],
Few-shot Open-set Recognition by Transformation Consistency,
CVPR21(12561-12570)
IEEE DOI 2111
Learning systems, Adaptation models, Prototypes, Estimation, Detectors, Pattern recognition BibRef

Yue, Z.Q.[Zhong-Qi], Wang, T.[Tan], Sun, Q.[Qianru], Hua, X.S.[Xian-Sheng], Zhang, H.[Hanwang],
Counterfactual Zero-Shot and Open-Set Visual Recognition,
CVPR21(15399-15409)
IEEE DOI 2111
Training, Visualization, Codes, Pattern recognition BibRef

Jafarzadeh, M.[Mohsen], Ahmad, T.[Touqeer], Dhamija, A.R.[Akshay Raj], Li, C.[Chunchun], Cruz, S.[Steve], Boult, T.E.[Terrance E.],
Automatic Open-World Reliability Assessment,
WACV21(1983-1992)
IEEE DOI 2106
Face recognition, Reliability theory, Reliability engineering, Classification algorithms, Reliability BibRef

Sachdeva, R.[Ragav], Cordeiro, F.R.[Filipe R.], Belagiannis, V.[Vasileios], Reid, I.D.[Ian D.], Carneiro, G.[Gustavo],
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels,
WACV21(3606-3614)
IEEE DOI 2106
Training, Deep learning, Uncertainty, Annotations, Semantics, Training data, Focusing BibRef

Miller, D.[Dimity], Sünderhauf, N.[Niko], Milford, M.[Michael], Dayoub, F.[Feras],
Class Anchor Clustering: A Loss for Distance-based Open Set Recognition,
WACV21(3569-3577)
IEEE DOI 2106
Training, Protocols, Neural networks, Training data, Benchmark testing BibRef

Yu, Q.[Qing], Ikami, D.[Daiki], Irie, G.[Go], Aizawa, K.[Kiyoharu],
Multi-task Curriculum Framework for Open-set Semi-supervised Learning,
ECCV20(XII: 438-454).
Springer DOI 2010
BibRef

Zhang, H.J.[Hong-Jie], Li, A.[Ang], Guo, J.[Jie], Guo, Y.[Yanwen],
Hybrid Models for Open Set Recognition,
ECCV20(III:102-117).
Springer DOI 2012
detect samples not belonging to any of the classes in its training set. BibRef

Bucci, S.[Silvia], Loghmani, M.R.[Mohammad Reza], Tommasi, T.[Tatiana],
On the Effectiveness of Image Rotation for Open Set Domain Adaptation,
ECCV20(XVI: 422-438).
Springer DOI 2010
BibRef

Chen, G.Y.[Guang-Yao], Qiao, L.M.[Li-Meng], Shi, Y.M.[Ye-Min], Peng, P.X.[Pei-Xi], Li, J.[Jia], Huang, T.J.[Tie-Jun], Pu, S.L.[Shi-Liang], Tian, Y.H.[Yong-Hong],
Learning Open Set Network with Discriminative Reciprocal Points,
ECCV20(III:507-522).
Springer DOI 2012
BibRef

Bertinetto, L.[Luca], Mueller, R.[Romain], Tertikas, K.[Konstantinos], Samangooei, S.[Sina], Lord, N.A.[Nicholas A.],
Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks,
CVPR20(12503-12512)
IEEE DOI 2008
Standards, Measurement, Vegetation, Machine learning, Art, Visualization, Pipelines BibRef

Liu, B., Kang, H., Li, H., Hua, G., Vasconcelos, N.M.,
Few-Shot Open-Set Recognition Using Meta-Learning,
CVPR20(8795-8804)
IEEE DOI 2008
Training, Measurement, Task analysis, Robustness, Entropy, Image recognition, Face recognition BibRef

Pan, Y., Yao, T., Li, Y., Ngo, C., Mei, T.,
Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation,
CVPR20(13864-13872)
IEEE DOI 2008
Adaptation models, Data structures, Mutual information, Data models, Training, Entropy, Task analysis BibRef

Kundu, J.N.[Jogendra Nath], Venkatesh, R.M.[Rahul Mysore], Venkat, N.[Naveen], Revanur, A.[Ambareesh], Babu, R.V.[R. Venkatesh],
Class-incremental Domain Adaptation,
ECCV20(XIII:53-69).
Springer DOI 2011
BibRef

Feng, Q., Kang, G., Fan, H., Yang, Y.,
Attract or Distract: Exploit the Margin of Open Set,
ICCV19(7989-7998)
IEEE DOI 2004
data handling, decision theory, pattern classification, set theory, domain adaptation, domain shift, semantic structure, open set data, Benchmark testing BibRef

Liu, H.[Hong], Cao, Z.J.[Zhang-Jie], Long, M.S.[Ming-Sheng], Wang, J.M.[Jian-Min], Yang, Q.A.[Qi-Ang],
Separate to Adapt: Open Set Domain Adaptation via Progressive Separation,
CVPR19(2922-2931).
IEEE DOI 2002
BibRef

Fu, J., Wu, X., Zhang, S., Yan, J.,
Improved Open Set Domain Adaptation with Backpropagation,
ICIP19(2506-2510)
IEEE DOI 1910
Open set domain adaptation, Back propagation, Symmetrical Kullback Leibler distance BibRef

Saito, K.[Kuniaki], Yamamoto, S.[Shohei], Ushiku, Y.[Yoshitaka], Harada, T.[Tatsuya],
Open Set Domain Adaptation by Backpropagation,
ECCV18(VI: 156-171).
Springer DOI 1810
BibRef

Yoshihashi, R.[Ryota], Shao, W.[Wen], Kawakami, R.[Rei], You, S.[Shaodi], Iida, M.[Makoto], Naemura, T.[Takeshi],
Classification-Reconstruction Learning for Open-Set Recognition,
CVPR19(4011-4020).
IEEE DOI 2002
BibRef

Tan, S.[Shuhan], Jiao, J.[Jiening], Zheng, W.S.[Wei-Shi],
Weakly Supervised Open-Set Domain Adaptation by Dual-Domain Collaboration,
CVPR19(5389-5398).
IEEE DOI 2002
BibRef

Perera, P.[Pramuditha], Morariu, V.I.[Vlad I.], Jain, R.[Rajiv], Manjunatha, V.[Varun], Wigington, C.[Curtis], Ordonez, V.[Vicente], Patel, V.M.[Vishal M.],
Generative-Discriminative Feature Representations for Open-Set Recognition,
CVPR20(11811-11820)
IEEE DOI 2008
Does the sample belong to one of the trained classes? Training, Task analysis, Force, Image reconstruction, Shape, Semantics BibRef

Oza, P.[Poojan], Patel, V.M.[Vishal M.],
C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition,
CVPR19(2302-2311).
IEEE DOI 2002
BibRef

Sun, L.[Li], Yu, X.Y.[Xiao-Yi], Wang, L.[Liuan], Sun, J.[Jun], Inakoshi, H.[Hiroya], Kobayashi, K.[Ken], Kobashi, H.[Hiromichi],
Automatic Neural Network Search Method for Open Set Recognition,
ICIP19(4090-4094)
IEEE DOI 1910
Neural network search, open set, search space, feature distribution, center loss BibRef

Neal, L.[Lawrence], Olson, M.[Matthew], Fern, X.L.[Xiao-Li], Wong, W.K.[Weng-Keen], Li, F.X.[Fu-Xin],
Open Set Learning with Counterfactual Images,
ECCV18(VI: 620-635).
Springer DOI 1810
Label known plus detect unknown classes. BibRef

Wang, Y.S.[Yi-Sen], Liu, W.Y.[Wei-Yang], Ma, X.J.[Xing-Jun], Bailey, J.[James], Zha, H.Y.[Hong-Yuan], Song, L.[Le], Xia, S.T.[Shu-Tao],
Iterative Learning with Open-set Noisy Labels,
CVPR18(8688-8696)
IEEE DOI 1812
Noise measurement, Feature extraction, Cats, Training, Training data, Labeling, Convolutional neural networks BibRef

Gao, H.[Hua], Ekenel, H.K.[Hazim Kemal], Stiefelhagen, R.[Rainer],
Robust Open-Set Face Recognition for Small-Scale Convenience Applications,
DAGM10(393-402).
Springer DOI 1009
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Contrastive Learning .