14.1.9.1 Unsupervised Few-Shot Learning, Semi-Supervised Few-Shot Learning

Huo, X.Y.[Xiao-Yang], Zeng, X.P.[Xiang-Ping], Wu, S.[Si], Wong, H.S.[Hau-San],
Attention Regularized Semi-Supervised Learning with Class-Ambiguous Data for Image Classification,
PR(129), 2022, pp. 108727.
Elsevier DOI 2206
Semi-supervised learning, Image classification, Attention regularization, Class-ambiguous data BibRef

Huo, X.Y.[Xiao-Yang], Zeng, X.P.[Xiang-Ping], Wu, S.[Si], Shen, W.J.[Wen-Jun], Wong, H.S.[Hau-San],
Collaborative Learning with Unreliability Adaptation for Semi-Supervised Image Classification,
PR(133), 2023, pp. 109032.
Elsevier DOI 2210
Semi-supervised learning, Image classification, Unreliability adaptation, Collaborative learning BibRef

Li, G.J.[Guo-Jie], Yu, Z.W.[Zhi-Wen], Yang, K.X.[Kai-Xiang], Chen, C.L.P.[C. L. Philip], Li, X.L.[Xue-Long],
Ensemble-Enhanced Semi-Supervised Learning With Optimized Graph Construction for High-Dimensional Data,
PAMI(47), No. 2, February 2025, pp. 1103-1119.
IEEE DOI 2501
Training, Noise measurement, Robustness, Collaboration, Smoothing methods, Noise, Laplace equations, Genetics, high-dimensional data BibRef

Li, J.C.[Ji-Chang], Wu, S.[Si], Liu, C.[Cheng], Yu, Z.W.[Zhi-Wen], Wong, H.S.[Hau-San],
Semi-Supervised Deep Coupled Ensemble Learning with Classification Landmark Exploration,
IP(29), No. 1, 2020, pp. 538-550.
IEEE DOI 1910
entropy, image classification, image matching, learning (artificial intelligence), neural nets, landmark learning BibRef

Wu, S.[Si], Ji, Q.J.[Qiu-Jia], Wang, S.F.[Shu-Feng], Wong, H.S.[Hau-San], Yu, Z.W.[Zhi-Wen], Xu, Y.[Yong],
Semi-Supervised Image Classification With Self-Paced Cross-Task Networks,
MultMed(20), No. 4, April 2018, pp. 851-865.
IEEE DOI 1804
Data models, Labeling, Predictive models, Semisupervised learning, Streaming media, Training, Image classification, semi-supervised learning BibRef

Huang, S.X.[Shi-Xin], Zeng, X.P.[Xiang-Ping], Wu, S.[Si], Yu, Z.W.[Zhi-Wen], Azzam, M.[Mohamed], Wong, H.S.[Hau-San],
Behavior regularized prototypical networks for semi-supervised few-shot image classification,
PR(112), 2021, pp. 107765.
Elsevier DOI 2102
Few-shot learning, Semi-supervised learning, Image classification, Prototypical networks BibRef

Zhu, L.C.[Lin-Chao], Yang, Y.[Yi],
Label Independent Memory for Semi-Supervised Few-Shot Video Classification,
PAMI(44), No. 1, January 2022, pp. 273-285.
IEEE DOI 2112
BibRef
Earlier:
Compound Memory Networks for Few-Shot Video Classification,
ECCV18(VII: 782-797).
Springer DOI 1810
Training, Feature extraction, Task analysis, Compounds, Dynamics, Data models, Prototypes, Few-shot video classification, compound memory networks BibRef

Zhang, B.[Bo], Ye, H.C.[Han-Cheng], Yu, G.[Gang], Wang, B.[Bin], Wu, Y.[Yike], Fan, J.Y.[Jia-Yuan], Chen, T.[Tao],
Sample-Centric Feature Generation for Semi-Supervised Few-Shot Learning,
IP(31), 2022, pp. 2309-2320.
IEEE DOI 2203
Task analysis, Data models, Measurement, Training, Semantics, Adaptation models, Benchmark testing, Few-shot learning, sample-centric BibRef

Xu, R.[Rui], Xing, L.[Lei], Shao, S.[Shuai], Zhao, L.F.[Li-Fei], Liu, B.[Baodi], Liu, W.F.[Wei-Feng], Zhou, Y.C.[Yi-Cong],
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning,
CirSysVideo(32), No. 12, December 2022, pp. 8674-8687.
IEEE DOI 2212
Feature extraction, Data mining, Finite element analysis, Training data, Semi-supervised learning, Few-shot learning, graph co-training (GCT) BibRef

Liu, F.[Fan], Yang, S.[Sai], Chen, D.[Delong], Huang, H.X.[Hua-Xi], Zhou, J.[Jun],
Few-shot classification guided by generalization error bound,
PR(145), 2024, pp. 109904.
Elsevier DOI 2311
Few-shot classification, Generalization error bound, Self-supervised learning, Knowledge distillation BibRef

Clay, V.[Viviane], Pipa, G.[Gordon], Kühnberger, K.U.[Kai-Uwe], König, P.[Peter],
Development of Few-Shot Learning Capabilities in Artificial Neural Networks When Learning Through Self-Supervised Interaction,
PAMI(46), No. 1, January 2024, pp. 209-219.
IEEE DOI 2312
BibRef

Gao, R.X.[Rui-Xuan], Su, H.[Han], Prasad, S.[Shitala], Tang, P.[Peisen],
Few-shot classification with multisemantic information fusion network,
IVC(141), 2024, pp. 104869.
Elsevier DOI 2402
Few-shot learning, Metric-based learning, Feature representation, Unsupervised mechanism BibRef

Wei, X.S.[Xiu-Shen], Xu, H.Y.[He-Yang], Yang, Z.W.[Zhi-Wen], Duan, C.L.[Chen-Long], Peng, Y.X.[Yu-Xin],
Negatives Make a Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning,
PAMI(46), No. 4, April 2024, pp. 2091-2103.
IEEE DOI 2403
Multiple signal classification, Task analysis, Training, Predictive models, Data models, Computational modeling, Robustness, few-shot learning BibRef

Liu, B.[Bing], Zhao, H.W.[Hong-Wei], Li, J.[Jiao], Gao, Y.S.[Yan-Sheng], Zhang, J.R.[Jiang-Rong],
SRL-ProtoNet: Self-supervised representation learning for few-shot remote sensing scene classification,
IET-CV(18), No. 7, 2024, pp. 1034-1042.
DOI Link 2411
image classification, natural scenes, remote sensing BibRef

Liu, Y.K.[Yu-Kun], Luo, Z.H.[Zhao-Hui], Shi, D.M.[Da-Ming],
A convex Kullback-Leibler optimization for semi-supervised few-shot learning,
CVIU(249), 2024, pp. 104152.
Elsevier DOI 2412
Few-shot learning, Convex function, Kullback-Leibler (KL) optimization, Expectation-maximization (EM) BibRef

Lazarou, M.[Michalis], Stathaki, T.[Tania], Avrithis, Y.[Yannis],
Exploiting unlabeled data in few-shot learning with manifold similarity and label cleaning,
PR(161), 2025, pp. 111304.
Elsevier DOI Code:
WWW Link. 2502
Few-shot learning, Semi-supervised learning, Transductive learning BibRef

Jing, K.[Kunlei], Ma, H.[Hebo], Zhang, C.[Chen], Wen, L.[Lei], Zhang, Z.R.[Zhao-Rui],
Recursive Confidence Training for Pseudo-Labeling Calibration in Semi-Supervised Few-Shot Learning,
IP(34), 2025, pp. 3194-3208.
IEEE DOI Code:
WWW Link. 2506
Entropy, Accuracy, Training, Calibration, Data models, Few shot learning, Labeling, Prototypes, Adaptation models, Reviews, recursive confidence training BibRef

Lu, Y.N.[Yu-Ning], Wen, L.J.[Liang-Jian], Liu, J.Z.[Jian-Zhuang], Liu, Y.J.[Ya-Jing], Tian, X.[Xinmei],
Self-Supervision Can Be a Good Few-Shot Learner,
ECCV22(XIX:740-758).
Springer DOI 2211
BibRef

Dong, B.[Bowen], Zhou, P.[Pan], Yan, S.C.[Shui-Cheng], Zuo, W.M.[Wang-Meng],
Self-Promoted Supervision for Few-Shot Transformer,
ECCV22(XX:329-347).
Springer DOI 2211
BibRef

Li, S.[Shuo], Liu, F.[Fang], Hao, Z.[Zehua], Zhao, K.B.[Kai-Bo], Jiao, L.C.[Li-Cheng],
Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space,
ECCV22(XXXI:420-436).
Springer DOI 2211
BibRef

Shirekar, O.K.[Ojas Kishore], Jamali-Rad, H.[Hadi],
Self-Supervised Class-Cognizant Few-Shot Classification,
ICIP22(976-980)
IEEE DOI 2211
Human intelligence, Dark matter, Benchmark testing, Iterative methods, Task analysis, Unsupervised learning, contrastive learning BibRef

Liu, Y.[Yang], Zhang, W.F.[Wei-Feng], Xiang, C.[Chao], Zheng, T.[Tu], Cai, D.[Deng], He, X.F.[Xiao-Fei],
Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification,
CVPR22(14391-14400)
IEEE DOI 2210
Atmospheric measurements, Markov processes, Particle measurements, Task analysis, Self- semi- meta- unsupervised learning BibRef

Ling, J.[Jie], Liao, L.[Lei], Yang, M.[Meng], Shuai, J.[Jia],
Semi-Supervised Few-shot Learning via Multi-Factor Clustering,
CVPR22(14544-14553)
IEEE DOI 2210
Manifolds, Learning systems, Codes, Fuses, Collaboration, Benchmark testing, Self- semi- meta- Recognition: detection, retrieval BibRef

Li, S.[Suichan], Chen, D.D.[Dong-Dong], Chen, Y.P.[Yin-Peng], Yuan, L.[Lu], Zhang, L.[Lei], Chu, Q.[Qi], Liu, B.[Bin], Yu, N.H.[Neng-Hai],
Improve Unsupervised Pretraining for Few-label Transfer,
ICCV21(10181-10190)
IEEE DOI 2203
Annotations, Computational modeling, Clustering algorithms, Representation learning, Vision applications and systems BibRef

Ma, J.W.[Jia-Wei], Xie, H.C.[Han-Chen], Han, G.X.[Guang-Xing], Chang, S.F.[Shih-Fu], Galstyan, A.[Aram], Abd-Almageed, W.[Wael],
Partner-Assisted Learning for Few-Shot Image Classification,
ICCV21(10553-10562)
IEEE DOI 2203
Training, Learning systems, Visualization, Annotations, Prototypes, Benchmark testing, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Das, R.[Rajshekhar], Wang, Y.X.[Yu-Xiong], Moura, J.M.F.[José M. F.],
On the Importance of Distractors for Few-Shot Classification,
ICCV21(9010-9020)
IEEE DOI 2203
Training, Codes, Stochastic processes, Performance gain, Task analysis, Transfer/Low-shot/Semi/Unsupervised Learning, Representation learning BibRef

Phoo, C.P.[Cheng Perng], Hariharan, B.[Bharath],
Coarsely-labeled Data for Better Few-shot Transfer,
ICCV21(9032-9041)
IEEE DOI 2203
Representation learning, Codes, Filtering, Buildings, Transfer/Low-shot/Semi/Unsupervised Learning, Representation learning BibRef

Lazarou, M.[Michalis], Stathaki, T.[Tania], Avrithis, Y.[Yannis],
Iterative label cleaning for transductive and semi-supervised few-shot learning,
ICCV21(8731-8740)
IEEE DOI 2203
Manifolds, Codes, Semisupervised learning, Prediction algorithms, Cleaning, Inference algorithms, Recognition and classification BibRef

Kang, D.[Dahyun], Kwon, H.[Heeseung], Min, J.[Juhong], Cho, M.[Minsu],
Relational Embedding for Few-Shot Classification,
ICCV21(8802-8813)
IEEE DOI 2203
Training, Visualization, Tensors, Image recognition, Correlation, Transforms, Transfer/Low-shot/Semi/Unsupervised Learning, Representation learning BibRef

Huang, K.[Kai], Geng, J.[Jie], Jiang, W.[Wen], Deng, X.Y.[Xin-Yang], Xu, Z.[Zhe],
Pseudo-loss Confidence Metric for Semi-supervised Few-shot Learning,
ICCV21(8651-8660)
IEEE DOI 2203
Measurement, Training, Weight measurement, Learning systems, Estimation, Multitasking, Extraterrestrial measurements, Recognition and classification BibRef

Yang, L.[Lihe], Zhuo, W.[Wei], Qi, L.[Lei], Shi, Y.H.[Ying-Huan], Gao, Y.[Yang],
Mining Latent Classes for Few-shot Segmentation,
ICCV21(8701-8710)
IEEE DOI 2203
Training, Costs, Codes, Training data, Prototypes, Benchmark testing, Transfer/Low-shot/Semi/Unsupervised Learning, Segmentation, grouping and shape BibRef

Fei, N.Y.[Nan-Yi], Gao, Y.Z.[Yi-Zhao], Lu, Z.W.[Zhi-Wu], Xiang, T.[Tao],
Z-Score Normalization, Hubness, and Few-Shot Learning,
ICCV21(142-151)
IEEE DOI 2203
Visualization, Prototypes, Benchmark testing, Boosting, Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Zhang, X.T.[Xue-Ting], Meng, D.B.[De-Bin], Gouk, H.[Henry], Hospedales, T.M.[Timothy M.],
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition,
ICCV21(631-640)
IEEE DOI 2203
Training, Representation learning, Measurement, Uncertainty, Memory management, Feature extraction, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Bateni, P.[Peyman], Barber, J.[Jarred], van de Meent, J.W.[Jan-Willem], Wood, F.[Frank],
Enhancing Few-Shot Image Classification with Unlabelled Examples,
WACV22(1597-1606)
IEEE DOI 2202
Training, Codes, Computational modeling, Benchmark testing, Feature extraction, Data mining, Transfer, Semi- and Un- supervised Learning BibRef

Zhang, H.G.[Hong-Guang], Koniusz, P.[Piotr], Jian, S.[Songlei], Li, H.D.[Hong-Dong], Torr, P.H.S.[Philip H. S.],
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning,
CVPR21(9427-9436)
IEEE DOI 2111
Training, Learning systems, Protocols, Annotations, Animals, Semantics BibRef

Chen, Z.Y.[Zheng-Yu], Ge, J.X.[Ji-Xie], Zhan, H.[Heshen], Huang, S.[Siteng], Wang, D.L.[Dong-Lin],
Pareto Self-Supervised Training for Few-Shot Learning,
CVPR21(13658-13667)
IEEE DOI 2111
Training, Pareto optimization, Benchmark testing, Space exploration, Task analysis BibRef

Chen, Z.T.[Zi-Tian], Maji, S.[Subhransu], Learned-Miller, E.G.[Erik G.],
Shot in the Dark: Few-Shot Learning with No Base-Class Labels,
LLID21(2662-2671)
IEEE DOI 2109
Supervised learning, Robustness BibRef

Nguyen, K.[Khoi], Todorovic, S.[Sinisa],
A Self-supervised GAN for Unsupervised Few-shot Object Recognition,
ICPR21(3225-3231)
IEEE DOI 2105
Training, Image coding, Performance gain, Probabilistic logic, Object recognition, Image reconstruction BibRef

Liu, X.Y.[Xin-Yue], Liu, P.X.[Peng-Xin], Zong, L.L.[Lin-Lin],
Transductive Prototypical Network For Few-Shot Classification,
ICIP20(1671-1675)
IEEE DOI 2011
Prototypes, Training, Testing, Task analysis, Neural networks, Semisupervised learning, Few-shot learning, transductive learning BibRef

Su, J.C.[Jong-Chyi], Maji, S.[Subhransu], Hariharan, B.[Bharath],
When Does Self-supervision Improve Few-shot Learning?,
ECCV20(VII:645-666).
Springer DOI 2011
BibRef

Monnier, T.[Tom], Vincent, E.[Elliot], Ponce, J.[Jean], Aubry, M.[Mathieu],
Unsupervised Layered Image Decomposition into Object Prototypes,
ICCV21(8620-8630)
IEEE DOI 2203
Social networking (online), Computational modeling, Prototypes, Predictive models, Benchmark testing, Image decomposition, Visual reasoning and logical representation BibRef

Yu, Z., Chen, L., Cheng, Z., Luo, J.,
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning,
CVPR20(12853-12861)
IEEE DOI 2008
Feature extraction, Training, Task analysis, Semisupervised learning, Data models, Entropy, Data mining BibRef

Zhu, H.[Hao], Koniusz, P.[Piotr],
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot Learning,
CVPR22(9068-9078)
IEEE DOI 2210
Learning systems, Codes, Benchmark testing, Data structures, Standards, Machine learning BibRef

Simon, C., Koniusz, P., Nock, R., Harandi, M.,
Adaptive Subspaces for Few-Shot Learning,
CVPR20(4135-4144)
IEEE DOI 2008
Prototypes, Task analysis, Feature extraction, Neural networks, Data models, Robustness, Machine learning BibRef

Dutta, A.[Anjan], Mancini, M.[Massimiliano], Akata, Z.[Zeynep],
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning,
DeepMTL21(2189-2198)
IEEE DOI 2112
Learning systems, Visualization, Protocols, Image recognition, Transfer learning, Semantics, Benchmark testing BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Cross-Domain Few-Shot Learning, Domain Adaption .