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 .