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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],
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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],
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Guided CNN for generalized zero-shot and open-set recognition using
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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,
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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
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 .