14.2.10 Open Set Recongnition

Chapter Contents (Back)
Open Set Recognition. Does the sample belong to training classes?

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

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
Computer vision, Deep learning, Image classification, Domain adaptation, Open set recognition, Positive-Unlabelled learning BibRef


Zisselman, E.[Ev], Tamar, A.[Aviv],
Deep Residual Flow for Out of Distribution Detection,
CVPR20(13991-14000)
IEEE DOI 2008
detecting out-of-distribution examples. Gaussian distribution, Neural networks, Data models, Training, Jacobian matrices, Computer architecture, Maximum likelihood detection 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], Venkat, N.[Naveen], Revanur, A.[Ambareesh], Rahul, M.V., Babu, R.V.[R. Venkatesh],
Towards Inheritable Models for Open-Set Domain Adaptation,
CVPR20(12373-12382)
IEEE DOI 2008
Adaptation models, Task analysis, Data models, Predictive models, Computational modeling, Data privacy, Training 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

Mundt, M., Pliushch, I., Majumder, S., Ramesh, V.,
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?,
SDL-CV19(753-757)
IEEE DOI 2004
Bayes methods, image classification, neural nets, object detection, statistical analysis, statistical distributions, out of distribution detection 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

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


Last update:Nov 23, 2020 at 10:27:11