14.2.5 Semi-Supervised Clustering, Semi-Supervised Learning, Classification

Verbeek, J.J.[Jakob J.], Vlassis, N.[Nikos],
Gaussian fields for semi-supervised regression and correspondence learning,
PR(39), No. 10, October 2006, pp. 1864-1875.
Elsevier DOI Gaussian fields; Regression; Active learning; Model selection 0606
BibRef

Ng, M.K.[Michael K.], Chan, E.Y.[Elaine Y.], So, M.M.C.[Meko M.C.], Ching, W.K.[Wai-Ki],
A semi-supervised regression model for mixed numerical and categorical variables,
PR(40), No. 6, June 2007, pp. 1745-1752.
Elsevier DOI 0704
Clustering; Regression; Data mining; Numerical variables; Categorical variables BibRef

Angelini, L.[Leonardo], Marinazzo, D.[Daniele], Pellicoro, M.[Mario], Stramaglia, S.[Sebastiano],
Semi-supervised learning by search of optimal target vector,
PRL(29), No. 1, 1 January 2008, pp. 34-39.
Elsevier DOI 0711
Semi-supervised learning; Kernel principal components; Transductive inference BibRef

Begleiter, R.[Ron], El-Yaniv, R.[Ran], Pechyony, D.[Dmitry],
Repairing self-confident active-transductive learners using systematic exploration,
PRL(29), No. 9, 1 July 2008, pp. 1245-1251.
Elsevier DOI 0711
Active learning; Transductive learning; Semi-supervised clustering BibRef

Come, E., Oukhellou, L., Denoeux, T., Aknin, P.,
Learning from partially supervised data using mixture models and belief functions,
PR(42), No. 3, March 2009, pp. 334-348.
Elsevier DOI 0811
Dempster-Shafer theory; Transferable belief model; Mixture models; EM algorithm; Classification; Clustering; Partially supervised learning; Semi-supervised learning BibRef

Wang, J.D.[Jing-Dong], Wang, F.[Fei], Zhang, C.S.[Chang-Shui], Shen, H.C.[Helen C.], Quan, L.[Long],
Linear Neighborhood Propagation and Its Applications,
PAMI(31), No. 9, September 2009, pp. 1600-1615.
IEEE DOI 0907
BibRef

Wang, F.[Fei], Zhang, C.S.[Chang-Shui], Shen, H.C.[Helen C.], Wang, J.D.[Jing-Dong],
Semi-Supervised Classification Using Linear Neighborhood Propagation,
CVPR06(I: 160-167).
IEEE DOI 0606
BibRef

Mallapragada, P.K.[Pavan Kumar], Jin, R.[Rong], Jain, A.K.[Anil K.], Liu, Y.[Yi],
SemiBoost: Boosting for Semi-Supervised Learning,
PAMI(31), No. 11, November 2009, pp. 2000-2014.
IEEE DOI 0910
BibRef
Earlier: A1, A2, A3, Only:
Active query selection for semi-supervised clustering,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Mallapragada, P.K.[Pavan K.], Jin, R.[Rong], Jain, A.K.[Anil K.],
Online visual vocabulary pruning using pairwise constraints,
CVPR10(3073-3080).
IEEE DOI 1006
BibRef

Liu, B., Wang, M., Hong, R., Zha, Z., Hua, X.S.,
Joint Learning of Labels and Distance Metric,
SMC-B(40), No. 3, June 2010, pp. 973-978.
IEEE DOI 1006
Deal with lack of training data and poor distance metrics. Semi-supervised learning. BibRef

Chen, K.[Ke], Wang, S.H.[Shi-Hai],
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions,
PAMI(33), No. 1, January 2011, pp. 129-143.
IEEE DOI 1011
BibRef

Chen, X.M.[Xiao-Ming], Liu, W.Q.[Wan-Quan], Qiu, H.N.[Hui-Ning], Lai, J.H.[Jian-Huang],
APSCAN: A parameter free algorithm for clustering,
PRL(32), No. 7, 1 May 2011, pp. 973-986.
Elsevier DOI 1101
Clustering algorithm; DBSCAN; Affinity propagation algorithm BibRef

Qiu, H.N.[Hui-Ning], Lai, J.H.[Jian-Huang], Huang, J.[Jian], Chen, Y.[Yu],
Semi-supervised discriminant analysis based on UDP regularization,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Adankon, M.M.[Mathias M.], Cheriet, M.[Mohamed],
Help-Training for semi-supervised support vector machines,
PR(44), No. 9, September 2011, pp. 2220-2230.
Elsevier DOI 1106
BibRef
Earlier:
Help-training for semi-supervised discriminative classifiers. Application to SVM,
ICPR08(1-4).
IEEE DOI 0812
Classification; Semi-supervised learning; SVM; Kernel machine
See also Optimizing resources in model selection for support vector machine. BibRef

Monaco, J.P., Madabhushi, A.,
Weighted Maximum Posterior Marginals for Random Fields Using an Ensemble of Conditional Densities From Multiple Markov Chain Monte Carlo Simulations,
MedImg(30), No. 7, July 2011, pp. 1353-1364.
IEEE DOI 1107
Adjust when some errors are more important than others. BibRef

Yang, T.[Ting], Priebe, C.E.[Carey E.],
The Effect of Model Misspecification on Semi-Supervised Classification,
PAMI(33), No. 10, October 2011, pp. 2093-2103.
IEEE DOI 1109
Training both on labeled and unlabeled observations. Using unlabeled points degrades result if the model is wrong. BibRef

Jiao, L.C., Shang, F.H.[Fan-Hua], Wang, F.[Fei], Liu, Y.Y.[Yuan-Yuan],
Fast semi-supervised clustering with enhanced spectral embedding,
PR(45), No. 12, December 2012, pp. 4358-4369.
Elsevier DOI 1208
Semi-supervised clustering (SSC); Side information; Spectral embedding; Pairwise constraint; Semantic gap BibRef

Yang, B.[Ben], Zhang, X.T.[Xue-Tao], Nie, F.P.[Fei-Ping], Wang, F.[Fei],
Fast Multiview Clustering With Spectral Embedding,
IP(31), 2022, pp. 3884-3895.
IEEE DOI 2206
Spectral analysis, Optimization, Complexity theory, Clustering methods, Clustering algorithms, Linear programming, orthogonality BibRef

Chang, C.C.[Chin-Chun], Chen, H.Y.[Hsin-Yi],
Semi-supervised clustering with discriminative random fields,
PR(45), No. 12, December 2012, pp. 4402-4413.
Elsevier DOI 1208
Semi-supervised clustering; Discriminative random fields BibRef

Liu, W., Wang, J., Chang, S.F.,
Robust and Scalable Graph-Based Semisupervised Learning,
PIEEE(100), No. 9, September 2012, pp. 2624-2638.
IEEE DOI 1209
BibRef

Wang, J.[Jun], Kumar, S.[Sanjiv], Chang, S.F.[Shih-Fu],
Semi-Supervised Hashing for Large-Scale Search,
PAMI(34), No. 12, December 2012, pp. 2393-2406.
IEEE DOI 1210
BibRef

Astudillo, C.A.[César A.], Oommen, B.J.[B. John],
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs,
PR(46), No. 1, January 2013, pp. 293-304.
Elsevier DOI 1209
SOM; Tree-based SOMs; Semi-supervised learning; Pattern recognition BibRef

Joshi, A.J.[Ajay J.], Porikli, F.M.[Fatih M.], Papanikolopoulos, N.P.[Nikolaos P.],
Scalable Active Learning for Multiclass Image Classification,
PAMI(34), No. 11, November 2012, pp. 2259-2273.
IEEE DOI 1209
BibRef
Earlier:
Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback,
CVPR10(2995-3002).
IEEE DOI 1006
BibRef
Earlier:
Multi-class active learning for image classification,
CVPR09(2372-2379).
IEEE DOI 0906
Multi-class learning to avoid large number of training samples. Interaction is only yes-no feedback, not precise category. Hundreds of categories. Scale linearly with size. BibRef

Fu, M., Luo, B., Kong, M.,
Semi-supervised manifold learning based on 2-fold weights,
IET-CV(6), No. 4, 2012, pp. 348-354.
DOI Link 1209
BibRef

Czaja, W.[Wojciech], Ehler, M.[Martin],
Schroedinger Eigenmaps for the Analysis of Biomedical Data,
PAMI(35), No. 5, May 2013, pp. 1274-1280.
IEEE DOI 1304
semi-supervised manifold learning BibRef

Wang, C.D.[Chang-Dong], Lai, J.H.[Jian-Huang], Suen, C.Y.[Ching Y.], Zhu, J.Y.[Jun-Yong],
Multi-Exemplar Affinity Propagation,
PAMI(35), No. 9, 2013, pp. 2223-2237.
IEEE DOI 1307
Affinity Propogation Clustering. MEAP. Belief propagation. BibRef

Huang, G.[Gao], Song, S.J.[Shi-Ji], Gupta, J.N.D.[Jatinder N.D.], Wu, C.[Cheng],
A second order cone programming approach for semi-supervised learning,
PR(46), No. 12, 2013, pp. 3548-3558.
Elsevier DOI 1308
Semi-supervised learning BibRef

Xing, X.L.[Xiang-Lei], Yu, Y.[Yao], Jiang, H.[Hua], Du, S.[Sidan],
A multi-manifold semi-supervised Gaussian mixture model for pattern classification,
PRL(34), No. 16, 2013, pp. 2118-2125.
Elsevier DOI 1310
Multi-manifold learning BibRef

He, P.[Ping], Xu, X.H.[Xiao-Hua], Hu, K.[Kongfa], Chen, L.[Ling],
Semi-supervised clustering via multi-level random walk,
PR(47), No. 2, 2014, pp. 820-832.
Elsevier DOI 1311
Semi-supervised clustering BibRef

Schwenker, F.[Friedhelm], Trentin, E.[Edmondo],
Partially supervised learning for pattern recognition,
PRL(37), No. 1, 2014, pp. 1-3.
Elsevier DOI 1402
BibRef

Schwenker, F.[Friedhelm], Trentin, E.[Edmondo],
Pattern classification and clustering: A review of partially supervised learning approaches,
PRL(37), No. 1, 2014, pp. 4-14.
Elsevier DOI 1402
Survey, Learning. Partially supervised learning BibRef

Lausser, L.[Ludwig], Schmid, F.[Florian], Schmid, M.[Matthias], Kestler, H.A.[Hans A.],
Unlabeling data can improve classification accuracy,
PRL(37), No. 1, 2014, pp. 15-23.
Elsevier DOI 1402
Partially supervised learning BibRef

Zeyl, T.J.[Timothy J.], Chau, T.[Tom],
A case study of linear classifiers adapted using imperfect labels derived from human event-related potentials,
PRL(37), No. 1, 2014, pp. 54-62.
Elsevier DOI 1402
Partially supervised learning BibRef

Lai, H.P.[Hien Phuong], Visani, M.[Muriel], Boucher, A.[Alain], Ogier, J.M.[Jean-Marc],
A new interactive semi-supervised clustering model for large image database indexing,
PRL(37), No. 1, 2014, pp. 94-106.
Elsevier DOI 1402
Semi-supervised clustering BibRef

Lai, H.P.[Hien Phuong], Visani, M.[Muriel], Boucher, A.[Alain], Ogier, J.M.[Jean-Marc],
Towards an interactive index structuring system for content-based image retrieval in large image databases,
ELCVIA(13), No. 2, 2014, pp. xx-yy.
DOI Link 1407
Ph.D.. Thesis. BibRef

Castelli, I.[Ilaria], Trentin, E.[Edmondo],
Combination of supervised and unsupervised learning for training the activation functions of neural networks,
PRL(37), No. 1, 2014, pp. 178-191.
Elsevier DOI 1402
Adaptive activation function BibRef

Yoshiyama, K., Sakurai, A.,
Laplacian minimax probability machine,
PRL(37), No. 1, 2014, pp. 192-200.
Elsevier DOI 1402
Semi-supervised learning BibRef

Le, T.B.[Thanh-Binh], Kim, S.W.[Sang-Woon],
Modified criterion to select useful unlabeled data for improving semi-supervised support vector machines,
PRL(60-61), No. 1, 2015, pp. 48-56.
Elsevier DOI 1506
Semi-supervised learning BibRef

Alkama, S.[Sadia], Desquesnes, X.[Xavier], El Moataz, A.[Abderrahim],
Infinity Laplacian on graphs with gradient terms for image and data clustering,
PRL(41), No. 1, 2014, pp. 65-72.
Elsevier DOI 1403
Weighted graphs BibRef

Biswas, A.[Arijit], Jacobs, D.W.[David W.],
Active Image Clustering with Pairwise Constraints from Humans,
IJCV(108), No. 1-2, May 2014, pp. 133-147.
WWW Link. 1405
BibRef
Earlier:
Active image clustering: Seeking constraints from humans to complement algorithms,
CVPR12(2152-2159).
IEEE DOI 1208
BibRef

Biswas, A.[Arijit], Jacobs, D.W.[David W.],
Active subclustering,
CVIU(125), No. 1, 2014, pp. 72-84.
Elsevier DOI 1406
Subclustering BibRef

Wang, H., Li, T., Li, T., Yang, Y.,
Constraint Neighborhood Projections for Semi-Supervised Clustering,
Cyber(44), No. 5, May 2014, pp. 636-643.
IEEE DOI 1405
Algorithm design and analysis BibRef

Xu, J., Wu, Q., Zhang, J., Shen, F., Tang, Z.,
Boosting Separability in Semisupervised Learning for Object Classification,
CirSysVideo(24), No. 7, July 2014, pp. 1197-1208.
IEEE DOI 1407
Algorithm design and analysis BibRef

Zhu, X.B.[Xi-Bin], Schleif, F.M.[Frank-Michael], Hammer, B.[Barbara],
Adaptive conformal semi-supervised vector quantization for dissimilarity data,
PRL(49), No. 1, 2014, pp. 138-145.
Elsevier DOI 1410
Semi-supervised learning BibRef

Schleif, F.M.[Frank-Michael], Zhu, X.B.[Xi-Bin], Gisbrecht, A.[Andrej], Hammer, B.[Barbara],
Fast approximated relational and kernel clustering,
ICPR12(1229-1232).
WWW Link. 1302
BibRef

Schleif, F.M.[Frank-Michael], Tino, P.[Peter],
Indefinite Core Vector Machine,
PR(71), No. 1, 2017, pp. 187-195.
Elsevier DOI 1707
Indefinite, learning BibRef

Schleif, F.M.[Frank-Michael], Raab, C.[Christoph], Tino, P.[Peter],
Sparsification of core set models in non-metric supervised learning,
PRL(129), 2020, pp. 1-7.
Elsevier DOI 2001
Large scale indefinite learning, Krein space, Sparse models, Orthogonal matching pursuit, Core sets, Non-metric learning BibRef

Li, Y.F., Zhou, Z.H.,
Towards Making Unlabeled Data Never Hurt,
PAMI(37), No. 1, January 2015, pp. 175-188.
IEEE DOI 1412
Data models. Unlabelled data hurts classification performance when included. BibRef

Ye, Y.D.[Yang-Dong], Liu, R.[Ruina], Lou, Z.Z.[Zheng-Zheng],
Incorporating side information into multivariate Information Bottleneck for generating alternative clusterings,
PRL(51), No. 1, 2015, pp. 70-78.
Elsevier DOI 1412
Alternative clustering. Find multiple alternative clusterings. BibRef

Shen, F.M.[Fu-Min], Shen, C.H.[Chun-Hua], Shi, Q.F.[Qin-Feng], van den Hengel, A.J.[Anton J.], Tang, Z.M.[Zhen-Min], Shen, H.T.,
Hashing on Nonlinear Manifolds,
IP(24), No. 6, June 2015, pp. 1839-1851.
IEEE DOI 1504
Binary codes BibRef
Earlier: A1, A2, A3, A4, A5, Only:
Inductive Hashing on Manifolds,
CVPR13(1562-1569)
IEEE DOI 1309
hashing; manifold learning Learning based hashing to model the structure in the data. BibRef

Shen, F.M.[Fu-Min], Shen, C.H.[Chun-Hua], Liu, W.[Wei], Shen, H.T.[Heng Tao],
Supervised Discrete Hashing,
CVPR15(37-45)
IEEE DOI 1510
BibRef

Liu, X., Guo, T., He, L., Yang, X.,
A Low-Rank Approximation-Based Transductive Support Tensor Machine for Semisupervised Classification,
IP(24), No. 6, June 2015, pp. 1825-1838.
IEEE DOI 1504
Classification algorithms BibRef

Zhao, M.B.[Ming-Bo], Zhan, C.J.[Chou-Jun], Wu, Z.[Zhou], Tang, P.[Peng],
Semi-Supervised Image Classification Based on Local and Global Regression,
SPLetters(22), No. 10, October 2015, pp. 1666-1670.
IEEE DOI 1506
extrapolation BibRef

Wang, D.[Di], Zhang, X.Q.[Xiao-Qin], Fan, M.Y.[Ming-Yu], Ye, X.Z.[Xiu-Zi],
An Efficient Semi-Supervised Classifier Based on Block-Polynomial Mapping,
SPLetters(22), No. 10, October 2015, pp. 1776-1780.
IEEE DOI 1506
computational complexity BibRef

Hong, Y.[Yi], Zhu, W.P.[Wei-Ping],
Spatial co-training for semi-supervised image classification,
PRL(63), No. 1, 2015, pp. 59-65.
Elsevier DOI 1508
Co-training BibRef

Lu, X.Q.[Xiao-Qiang], Li, X.L.[Xue-Long], Mou, L.C.[Li-Chao],
Semi-Supervised Multitask Learning for Scene Recognition,
Cyber(45), No. 9, September 2015, pp. 1967-1976.
IEEE DOI 1509
feature selection BibRef

Zhuang, L.S.[Lian-Sheng], Gao, S.H.[Sheng-Hua], Tang, J.H.[Jin-Hui], Wang, J.J.[Jing-Jing], Lin, Z.C.[Zhou-Chen], Ma, Y.[Yi], Yu, N.H.[Neng-Hai],
Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features,
IP(24), No. 11, November 2015, pp. 3717-3728.
IEEE DOI 1509
feature extraction BibRef

Zhuang, L.S.[Lian-Sheng], Gao, H.Y.[Hao-Yuan], Lin, Z.C.[Zhou-Chen], Ma, Y.[Yi], Zhang, X.[Xin], Yu, N.H.[Neng-Hai],
Non-Negative Low Rank and Sparse Graph for Semi-Supervised Learning,
CVPR12(2328-2335).
IEEE DOI 1208
BibRef

Zhuang, L.S.[Lian-Sheng], Gao, H.Y.[Hao-Yuan], Huang, J.J.[Jing-Jing], Yu, N.H.[Neng-Hai],
Semi-supervised Classification via Low Rank Graph,
ICIG11(511-516).
IEEE DOI 1109
BibRef

Yang, Y.[Yun], Liu, X.C.[Xing-Chen],
A robust semi-supervised learning approach via mixture of label information,
PRL(68, Part 1), No. 1, 2015, pp. 15-21.
Elsevier DOI 1512
Semi-supervised learning BibRef

Hernández-González, J.[Jerónimo], Inza, I.[Iñaki], Lozano, J.A.[Jose A.],
Weak supervision and other non-standard classification problems: A taxonomy,
PRL(69), No. 1, 2016, pp. 49-55.
Elsevier DOI 1601
Weakly supervised classification BibRef

Loog, M.[Marco],
Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification,
PAMI(38), No. 3, March 2016, pp. 462-475.
IEEE DOI 1602
Covariance matrices BibRef

Gan, H.T.[Hai-Tao], Luo, Z.Z.[Zhi-Zeng], Fan, Y.L.[Ying-Le], Sang, N.[Nong],
Enhanced manifold regularization for semi-supervised classification,
JOSA-A(33), No. 6, June 2016, pp. 1207-1213.
DOI Link 1606
Pattern recognition BibRef

Zhao, Z.Q.[Zhi-Qiang], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Zhao, J.Q.[Jia-Qi], Chen, P.H.[Pu-Hua],
Semisupervised Discriminant Feature Learning for SAR Image Category via Sparse Ensemble,
GeoRS(54), No. 6, June 2016, pp. 3532-3547.
IEEE DOI 1606
feature extraction BibRef

Amorim, W.P.[Willian P.], Falcão, A.X.[Alexandre X.], Papa, J.P.[João P.], Carvalho, M.H.[Marcelo H.],
Improving semi-supervised learning through optimum connectivity,
PR(60), No. 1, 2016, pp. 72-85.
Elsevier DOI 1609
Semi-supervised learning BibRef

Yu, H.B.[Hong-Bin], Lu, H.T.[Hong-Tao],
Orthogonal optimal reverse prediction for semi-supervised learning,
PR(60), No. 1, 2016, pp. 908-920.
Elsevier DOI 1609
Semi-supervised learning BibRef

Song, J.K.[Jing-Kuan], Gao, L.L.[Lian-Li], Nie, F.P.[Fei-Ping], Shen, H.T.[Heng Tao], Yan, Y.[Yan], Sebe, N.[Nicu],
Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation,
IP(25), No. 11, November 2016, pp. 4999-5011.
IEEE DOI 1610
BibRef
Earlier: A2, A1, A3, A6, A4, A5:
Optimal graph learning with partial tags and multiple features for image and video annotation,
CVPR15(4371-4379)
IEEE DOI 1510
graph theory. Use untagged data to improve supervised learning. BibRef

Song, J.K.[Jing-Kuan], Gao, L.L.[Lian-Li], Zou, F.[Fuhao], Yan, Y.[Yan], Sebe, N.[Nicu],
Deep and fast: Deep learning hashing with semi-supervised graph construction,
IVC(55, Part 2), No. 1, 2016, pp. 101-108.
Elsevier DOI 1612
Deep learning BibRef

Zhao, Z.[Zhuang], Qi, W.[Wei], Han, J.[Jing], Zhang, Y.[Yi], Bai, L.F.[Lian-Fa],
Semi-supervised classification via discriminative sparse manifold regularization,
SP:IC(47), No. 1, 2016, pp. 207-217.
Elsevier DOI 1610
Manifold regularization BibRef

Zhao, Z.[Zhuang], Bai, L.[Lianfa], Zhang, Y.[Yi], Han, J.[Jing],
Classification via semi-supervised multi-random subspace sparse representation,
SIViP(13), No. 7, October 2019, pp. 1387-1394.
WWW Link. 1911
BibRef

Samat, A.[Alim], Gamba, P.[Paolo], Liu, S.C.[Si-Cong], Du, P.J.[Pei-Jun], Abuduwaili, J.[Jilili],
Jointly Informative and Manifold Structure Representative Sampling Based Active Learning for Remote Sensing Image Classification,
GeoRS(54), No. 11, November 2016, pp. 6803-6817.
IEEE DOI 1610
Kernel BibRef

Liu, W.H.[Wen-He], Gao, C.Q.[Chen-Qiang], Chang, X.J.[Xiao-Jun], Wu, Q.[Qun],
Unified discriminating feature analysis for visual category recognition,
JVCIR(40, Part B), No. 1, 2016, pp. 772-778.
Elsevier DOI 1610
Visual category recognition. Feature selection BibRef

Dornaika, F.[Fadi], El Traboulsi, Y.[Youssof],
Matrix exponential based semi-supervised discriminant embedding for image classification,
PR(61), No. 1, 2017, pp. 92-103.
Elsevier DOI 1705
Graph-based semi-supervised learning BibRef

Dornaika, F.[Fadi], Baradaaji, A., El Traboulsi, Y.[Youssof],
Soft Label and Discriminant Embedding Estimation for Semi-Supervised Classification,
ICPR21(7250-7257)
IEEE DOI 2105
Estimation, Semisupervised learning, Prediction algorithms, Feature extraction, Iterative algorithms, image categorization BibRef

Dornaika, F.[Fadi], El Traboulsi, Y.[Youssof],
Margin Based Semi-Supervised Elastic Embedding for Face Image Analysis,
Manifold17(1313-1320)
IEEE DOI 1802
Face, Laplace equations, Manifolds, Semisupervised learning, Supervised learning, Symmetric matrices, Training, semi-supervised learning BibRef

Krijthe, J.H.[Jesse H.], Loog, M.[Marco],
Robust semi-supervised least squares classification by implicit constraints,
PR(63), No. 1, 2017, pp. 115-126.
Elsevier DOI 1612
BibRef
And:
Optimistic semi-supervised least squares classification,
ICPR16(1677-1682)
IEEE DOI 1705
Convergence, Encoding, Labeling, Linear programming, Optimization, Semisupervised learning, Training BibRef

Gu, N., Fan, M., Meng, D.,
Robust Semi-Supervised Classification for Noisy Labels Based on Self-Paced Learning,
SPLetters(23), No. 12, December 2016, pp. 1806-1810.
IEEE DOI 1612
data handling BibRef

Yue, Z.S.[Zong-Sheng], Meng, D.Y.[De-Yu], He, J.[Juan], Zhang, G.[Gemeng],
Semi-supervised learning through adaptive Laplacian graph trimming,
IVC(60), No. 1, 2017, pp. 38-47.
Elsevier DOI 1704
Semi-supervised learning BibRef

Liu, P.C.[Peng-Cheng], Yang, P.P.[Pei-Pei], Wang, C., Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
A Semi-Supervised Method for Surveillance-Based Visual Location Recognition,
Cyber(47), No. 11, November 2017, pp. 3719-3732.
IEEE DOI 1710
Cameras, Image recognition, Meteorology, Mobile communication, Mobile handsets, Surveillance, Visualization, Cross-device (C-D) recognition, semi-supervised learning, visual, localization BibRef

Liu, P.C.[Peng-Cheng], Yang, P.P.[Pei-Pei], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu], Hao, H.W.[Hong-Wei],
Semi-supervised Learning for Cross-Device Visual Location Recognition,
ICPR14(2873-2878)
IEEE DOI 1412
Cameras BibRef

Wang, Y.Y.[Yun-Yun], Meng, Y.[Yan], Li, Y.[Yun], Chen, S.C.[Song-Can], Fu, Z.Y.[Zhen-Yong], Xue, H.[Hui],
Semi-supervised manifold regularization with adaptive graph construction,
PRL(98), No. 1, 2017, pp. 90-95.
Elsevier DOI 1710
Semi-supervised classification BibRef

Wigness, M.[Maggie], Draper, B.A.[Bruce A.], Beveridge, J.R.[J. Ross],
Efficient Label Collection for Image Datasets via Hierarchical Clustering,
IJCV(126), No. 1, January 2018, pp. 59-85.
Springer DOI 1801
BibRef
Earlier:
Efficient label collection for unlabeled image datasets,
CVPR15(4594-4602)
IEEE DOI 1510
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

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

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

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Semi-Supervised Learning for Discrete Choice Models,
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Wang, X.[Xiao], Kihara, D.[Daisuke], Luo, J.B.[Jie-Bo], Qi, G.J.[Guo-Jun],
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Noise measurement, Data models, Training, Reliability, Task analysis, Predictive models, Image segmentation, Self-training, recognition BibRef

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Clustering algorithms, Matrix converters, Machine learning algorithms, Optimization, Benchmark testing, consensus of constraints BibRef

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Semisupervised learning, Predictive models, Training, Entropy, Task analysis, Integrated circuit modeling, Data models, deep learning BibRef

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GAN, Embedding, Semi-supervised learning, Structured data BibRef

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Concept factorization, Semi-supervised learning, Clustering, Nonnegative matrix factorization, Kernel method BibRef

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Semisupervised learning, Data models, Task analysis, Supervised learning, Costs, Adaptation models, Data mining, propagation acceleration BibRef

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Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results,
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Semisupervised learning, Manifolds, Standards, Geometry, Complexity theory, Task analysis, Supervised learning, assumptions BibRef

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Semi-supervised vector-valued learning: Improved bounds and algorithms,
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IEEE DOI 2304
Predictive models, Training, Data models, Semantics, Perturbation methods, Computational modeling, Benchmark testing, image classification BibRef

Huang, Z.[Zhuo], Yang, J.[Jian], Gong, C.[Chen],
They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning,
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IEEE DOI 2306
Training, Recycling, Feature extraction, Data models, Birds, Semisupervised learning, Semi-supervised learning, domain adaptation BibRef

Jia, N.[Nan], Tian, X.L.[Xiao-Lin], Gao, W.X.[Wen-Xing], Jiao, L.C.[Li-Cheng],
Deep Graph-Convolutional Generative Adversarial Network for Semi-Supervised Learning on Graphs,
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Graph Laplacian for Semi-supervised Learning,
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Hierarchical Bias Mitigation for Semi-Supervised Medical Image Classification,
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IEEE DOI 2308

WWW Link. Feature extraction, Labeling, Predictive models, Biomedical imaging, Optimization, Training, consistency-aware heredity BibRef

Bao, J.Q.[Jia-Qi], Kudo, M.[Mineichi], Kimura, K.[Keigo], Sun, L.[Lu],
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Feature selection, Semi-supervised learning, Ridge regression, Nuclear norm BibRef

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Semi-supervised learning, Pseudo labeling, Dempster-Shafer theory BibRef

Tao, H.[Hong], Jiang, J.C.[Jia-Cheng], Hou, C.P.[Chen-Ping], Luo, T.J.[Ting-Jin], Fan, R.D.[Rui-Dong], Zhang, J.[Jing],
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Face recognition, Compounds, Image reconstruction, Clustering methods, Clustering algorithms, Task analysis, incomplete view learning BibRef

Chen, Y.B.[Yan-Bei], Mancini, M.[Massimiliano], Zhu, X.T.[Xia-Tian], Akata, Z.[Zeynep],
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey,
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Survey, Semi-Supervised Learning. Data models, Visualization, Training, Task analysis, Semisupervised learning, Deep learning, Unsupervised learning, visual representation learning BibRef

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Pairwise constraints, Semi-supervised clustering, Sample similarity BibRef

Zhao, J.G.[Jia-Guo], Zhang, J.J.[Jun-Jie], Huang, H.X.[Hua-Xi], Zhang, J.[Jian],
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Liu, Q.[Qiang], Yue, J.[Jun], Kuang, Y.[Yang], Xie, W.Y.[Wei-Ying], Fang, L.Y.[Le-Yuan],
SemiRS-COC: Semi-Supervised Classification for Complex Remote Sensing Scenes With Cross-Object Consistency,
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IEEE DOI 2407
Data models, Feature extraction, Predictive models, Interference, Labeling, Image color analysis, Task analysis, Image processing, cross-object consistency BibRef

Li, M.Y.[Ming-Yu], Zhou, T.[Tao], Han, B.[Bo], Liu, T.L.[Tong-Liang], Liang, X.[Xinkai], Zhao, J.J.[Jia-Jia], Gong, C.[Chen],
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IEEE DOI 2408
Prototypes, Training, Semisupervised learning, Perturbation methods, Entropy, Dogs, Computer science, semi-supervised learning BibRef

Pan, Z.Y.[Zhi-Yu], Cui, J.H.[Jia-Hao], Wang, K.W.[Ke-Wei], Wu, Y.Z.[Yi-Zheng], Cao, Z.G.[Zhi-Guo],
Pseudo Label Fusion With Uncertainty Estimation for Semi-Supervised Cropping Box Regression,
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IEEE DOI 2408
Task analysis, Uncertainty, Annotations, Semisupervised learning, Object detection, Data models, Multitasking, Image cropping, uncertainty estimation BibRef

Long, Z.G.[Zhi-Guo], Gao, Y.[Yang], Meng, H.[Hua], Chen, Y.[Yuxu], Kou, H.[Hui],
Semi-supervised clustering guided by pairwise constraints and local density structures,
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Semi-supervised clustering, Local density peaks, Pairwise constraint propagation, Inter-cluster conflict resolution BibRef

Hou, R.B.[Rui-Bing], Chang, H.[Hong], Ma, B.P.[Bing-Peng], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Triplet Adaptation Framework for Robust Semi-Supervised Learning,
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IEEE DOI 2411
Degradation, Adaptation models, Buildings, Semisupervised learning, Semi-supervised learning, robust semi-supervised learning, distribution inconsistency BibRef

Yang, Y.[Yang], Jiang, N.[Nan], Xu, Y.[Yi], Zhan, D.C.[De-Chuan],
Robust Semi-Supervised Learning by Wisely Leveraging Open-Set Data,
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IEEE DOI 2411
Data models, Training, Task analysis, Semisupervised learning, Biological system modeling, Computational modeling, open-set data BibRef

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Targeted Representation Alignment for Open-World Semi-Supervised Learning,
CVPR24(23072-23082)
IEEE DOI 2410
Codes, Prototypes, Clustering algorithms, Semisupervised learning, Benchmark testing, neural collapse BibRef

Zhang, F.[Fan], Hua, X.S.[Xian-Sheng], Chen, C.[Chong], Luo, X.[Xiao],
Fine-grained Prototypical Voting with Heterogeneous Mixup for Semi-supervised 2D-3D Cross-modal Retrieval,
CVPR24(17016-17026)
IEEE DOI 2410
Solid modeling, Semantics, Noise, Prototypes, Benchmark testing, Data models, Cross-modal Retrieval, Semi-supervised Learning BibRef

Fooladgar, F.[Fahimeh], To, M.N.N.[Minh Nguyen Nhat], Mousavi, P.[Parvin], Abolmaesumi, P.[Purang],
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise,
VAND24(4012-4021)
IEEE DOI Code:
WWW Link. 2410
Manifolds, Representation learning, Noise, Semantics, Training data, Clustering algorithms, Contrastive learning, noisy labels, self-supervised learning BibRef

Ihler, S.[Sontje], Kuhnke, F.[Felix], Kuhlgatz, T.[Timo], Seel, T.[Thomas],
Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert,
EnhanceMedIm24(2295-2304)
IEEE DOI 2410
Semisupervised learning, Performance gain, Solids, Filling, Robustness, Mirrors, Multi-Label Classification BibRef

Yue, X.[Xin], Lu, Z.Q.[Zong-Qing], Lin, X.R.[Xiang-Ru], Ren, W.J.[Wen-Jia], Shao, Z.J.[Zhi-Jing], Hu, H.[Haonan], Zhang, Y.[Yu], Liao, Q.M.[Qing-Min],
Semi-Stereo: A Universal Stereo Matching Framework for Imperfect Data via Semi-supervised Learning,
L3D24(646-655)
IEEE DOI 2410
Measurement, Computer network reliability, Semantics, Semisupervised learning, Benchmark testing, stereo matching, imperfect data BibRef

Majurski, M.[Michael], Menon, S.[Sumeet], Farvardin, P.[Parniyan], Chapman, D.[David],
A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning,
ZeroShot24(7809-7818)
IEEE DOI Code:
WWW Link. 2410
Deep learning, Sensitivity, Semisupervised learning, Predictive models, Polynomials, Distance measurement, Generative Modeling BibRef

Huang, Z.[Zhe], Jiang, R.J.[Rui-Jie], Aeron, S.[Shuchin], Hughes, M.C.[Michael C.],
Systematic comparison of semi-supervised and self-supervised learning for medical image classification,
CVPR24(22282-22293)
IEEE DOI 2410
Training, Measurement, Accuracy, Systematics, Protocols, Self-supervised learning, Benchmark testing, Semi-supervised, Self-supervised BibRef

Tanaka, Y.[Yuki], Yoshida, S.M.[Shuhei M.], Shibata, T.[Takashi], Terao, M.[Makoto], Okatani, T.[Takayuki], Sugiyama, M.[Masashi],
Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled Data,
WACV24(2768-2777)
IEEE DOI 2404
Training, Visualization, Semisupervised learning, Data augmentation, Algorithms, Machine learning architectures, Image recognition and understanding BibRef

Nguyen, K.B.[Khanh-Binh],
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector,
WACV24(2430-2439)
IEEE DOI Code:
WWW Link. 2404
Training, Costs, Error analysis, Pipelines, Self-supervised learning, Semisupervised learning, Algorithms, Image recognition and understanding BibRef

Nguyen, K.B.[Khanh-Binh],
SequenceMatch Revisiting the design of weak-strong augmentations for Semi-supervised learning,
WACV24(96-105)
IEEE DOI Code:
WWW Link. 2404
Training, Error analysis, Pipelines, Semisupervised learning, Predictive models, Benchmark testing, Data augmentation, Image recognition and understanding BibRef

Nguyen, K.B.[Khanh-Binh], Yang, J.S.[Joon-Sung],
Boosting Semi-Supervised Learning by bridging high and low-confidence predictions,
LIMIT23(1020-1030)
IEEE DOI 2401
BibRef

Xu, H.M.[Hai-Ming], Liu, L.Q.[Ling-Qiao], Chen, H.[Hao], Abbasnejad, E.[Ehsan], Felix, R.[Rafael],
Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models,
VCL23(3284-3294)
IEEE DOI 2401
BibRef

Pirvu, M.[Mihai], Marcu, A.[Alina], Dobrescu, A.[Alexandra], Belbachir, N.[Nabil], Leordeanu, M.[Marius],
Multi-Task Hypergraphs for Semi-supervised Learning using Earth Observations,
VCL23(3396-3406)
IEEE DOI 2401
BibRef

Fan, Y.[Yue], Kukleva, A.[Anna], Dai, D.X.[Deng-Xin], Schiele, B.[Bernt],
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning,
ICCV23(16022-16032)
IEEE DOI Code:
WWW Link. 2401
BibRef

Yang, L.[Lihe], Zhao, Z.[Zhen], Qi, L.[Lei], Qiao, Y.[Yu], Shi, Y.[Yinghuan], Zhao, H.S.[Heng-Shuang],
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning,
ICCV23(16141-16150)
IEEE DOI 2401
BibRef

Ma, Q.K.[Qian-Kun], Gao, J.[Jiyao], Zhan, B.[Bo], Guo, Y.P.[Yun-Peng], Zhou, J.[Jiliu], Wang, Y.[Yan],
Rethinking Safe Semi-supervised Learning: Transferring the Open-set Problem to A Close-set One,
ICCV23(16324-16333)
IEEE DOI 2401
BibRef

Du, P.[Pan], Zhao, S.[Suyun], Sheng, Z.[Zisen], Li, C.P.[Cui-Ping], Chen, H.[Hong],
Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatch,
ICCV23(16364-16374)
IEEE DOI Code:
WWW Link. 2401
BibRef

Zheng, M.[Mingkai], You, S.[Shan], Huang, L.[Lang], Luo, C.[Chen], Wang, F.[Fei], Qian, C.[Chen], Xu, C.[Chang],
SimMatchV2: Semi-Supervised Learning with Graph Consistency,
ICCV23(16386-16396)
IEEE DOI 2401
BibRef

Zhao, B.C.[Bing-Chen], Wen, X.[Xin], Han, K.[Kai],
Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery,
ICCV23(16577-16587)
IEEE DOI Code:
WWW Link. 2401
BibRef

Gui, G.[Guan], Zhao, Z.[Zhen], Qi, L.[Lei], Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Shi, Y.[Yinghuan],
Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning,
ICCV23(15834-15843)
IEEE DOI Code:
WWW Link. 2401
BibRef

Li, Z.K.[Ze-Kun], Qi, L.[Lei], Shi, Y.[Yinghuan], Gao, Y.[Yang],
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization,
ICCV23(15824-15833)
IEEE DOI Code:
WWW Link. 2401
BibRef

Duan, Y.[Yue], Zhao, Z.[Zhen], Qi, L.[Lei], Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Shi, Y.[Yinghuan],
Towards Semi-supervised Learning with Non-random Missing Labels,
ICCV23(16075-16085)
IEEE DOI Code:
WWW Link. 2401
BibRef

Hernandez-Sequeira, I.[Itza], Fernandez-Beltran, R.[Ruben], Xu, Y.H.[Yong-Hao], Ghamisi, P.[Pedram], Pla, F.[Filiberto],
Semi-supervised Classification for Remote Sensing Datasets,
CIAP23(I:463-474).
Springer DOI 2312
BibRef

Karaliolios, N.[Nikolaos], Chabot, F.[Florian], Dupont, C.[Camille], Le Borgne, H.[Hervé], Pham, Q.C.[Quoc-Cuong], Audigier, R.[Romaric],
Generalized Pseudo-Labeling in Consistency Regularization for Semi-Supervised Learning,
ICIP23(525-529)
IEEE DOI 2312
BibRef

Chen, X.X.[Xiao-Xue], Zheng, Y.H.[Yu-Hang], Zheng, Y.P.[Yu-Peng], Zhou, Q.[Qiang], Zhao, H.[Hao], Zhou, G.[Guyue], Zhang, Y.Q.[Ya-Qin],
DPF: Learning Dense Prediction Fields with Weak Supervision,
CVPR23(15347-15357)
IEEE DOI 2309
BibRef

Nassar, I.[Islam], Hayat, M.[Munawar], Abbasnejad, E.[Ehsan], Rezatofighi, H.[Hamid], Haffari, G.[Gholamreza],
Protocon: Pseudo-Label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-Supervised Learning,
CVPR23(11641-11650)
IEEE DOI 2309
BibRef

Sosea, T.[Tiberiu], Caragea, C.[Cornelia],
MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins,
CVPR23(15773-15782)
IEEE DOI 2309
BibRef

Li, L.J.[Li-Jian], Zhang, Y.H.[Yun-He], Huang, A.[Aiping],
Learnable Subspace Orthogonal Projection for Semi-supervised Image Classification,
ACCV22(III:477-490).
Springer DOI 2307
BibRef

Masud, U.[Umar], Cohen, E.[Ethan], Bendidi, I.[Ihab], Bollot, G.[Guillaume], Genovesio, A.[Auguste],
Comparison of Semi-supervised Learning Methods for High Content Screening Quality Control,
BioImage22(395-405).
Springer DOI 2304
BibRef

Chen, Z.M.[Zhi-Min], Jing, L.L.[Long-Long], Yang, L.[Liang], Li, Y.W.[Ying-Wei], Li, B.[Bing],
Class-Level Confidence Based 3D Semi-Supervised Learning,
WACV23(633-642)
IEEE DOI 2302
Estimation, Semisupervised learning, Task analysis, Algorithms: Machine learning architectures, formulations, 3D computer vision BibRef

Dubost, F.[Florian], Hong, E.[Erin], Tang, S.Y.[Si-Yi], Bhaskhar, N.[Nandita], Lee-Messer, C.[Christopher], Rubin, D.[Daniel],
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing,
WACV23(1890-1899)
IEEE DOI 2302
Training, Event detection, Neural networks, Training data, Machine learning, Semisupervised learning, Mathematical models, Social good BibRef

Xu, H.Y.[Hai-Yun], Huang, L.[Lili], Jiang, B.[Bo], Tang, J.[Jin], Zhang, S.J.[Shao-Jie],
Semi-supervised Learning via Multiple Layer Graph Regularized Perception,
ICPR22(3112-3118)
IEEE DOI 2212
Message passing, Supervised learning, Semisupervised learning, Logic gates, Propagation losses, Robustness, Topology BibRef

Wallin, E.[Erik], Svensson, L.[Lennart], Kahl, F.[Fredrik], Hammarstrand, L.[Lars],
Improving Open-Set Semi-Supervised Learning with Self-Supervision,
WACV24(2345-2354)
IEEE DOI Code:
WWW Link. 2404
BibRef
Earlier:
DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision,
ICPR22(2871-2877)
IEEE DOI 2212
Training, Codes, Employment, Focusing, Semisupervised learning, Benchmark testing, Algorithms, Machine learning architectures, and algorithms. Supervised learning, Fitting, Training data, Predictive models BibRef

Schmarje, L.[Lars], Santarossa, M.[Monty], Schröder, S.M.[Simon-Martin], Zelenka, C.[Claudius], Kiko, R.[Rainer], Stracke, J.[Jenny], Volkmann, N.[Nina], Koch, R.[Reinhard],
A Data-Centric Approach for Improving Ambiguous Labels with Combined Semi-supervised Classification and Clustering,
ECCV22(VIII:363-380).
Springer DOI 2211
BibRef

Wang, Z.W.[Zhuo-Wei], Jiang, J.[Jing], Long, G.D.[Guo-Dong],
Positive Unlabeled Learning by Semi-Supervised Learning,
ICIP22(2976-2980)
IEEE DOI 2211
Training, Degradation, Semisupervised learning, Benchmark testing, Image Classification, Positive-Unlabeled Learning, Semi-Supervised Learning BibRef

Kingetsu, H.[Hiroaki], Kobayashi, K.[Kenichi], Okawa, Y.[Yoshihiro], Yokota, Y.[Yasuto], Nakazawa, K.[Katsuhito],
Multi-Step Test-Time Adaptation with Entropy Minimization and Pseudo-Labeling,
ICIP22(4153-4157)
IEEE DOI 2211
Training, Adaptation models, Neural networks, Training data, Semisupervised learning, Predictive models, Test-Time Adaptation, Robustness BibRef

Tang, H.[Hui], Sun, L.[Lin], Jia, K.[Kui],
Stochastic Consensus: Enhancing Semi-Supervised Learning with Consistency of Stochastic Classifiers,
ECCV22(XXXI:330-346).
Springer DOI 2211
BibRef

Rizve, M.N.[Mamshad Nayeem], Kardan, N.[Navid], Shah, M.[Mubarak],
Towards Realistic Semi-supervised Learning,
ECCV22(XXXI:437-455).
Springer DOI 2211
BibRef

Yuan, X.K.[Xin-Kai], Li, Z.L.[Zi-Linghan], Wang, G.[Gaoang],
ActiveMatch: End-To-End Semi-Supervised Active Representation Learning,
ICIP22(1136-1140)
IEEE DOI 2211
Training, Representation learning, Semisupervised learning, Human in the loop, Data models, Classification algorithms, contrastive learning BibRef

Hu, H.T.[Heng-Tong], Xie, L.X.[Ling-Xi], Huo, X.Y.[Xin-Yue], Hong, R.C.[Ri-Chang], Tian, Q.[Qi],
Vibration-Based Uncertainty Estimation for Learning from Limited Supervision,
ECCV22(XXX:160-176).
Springer DOI 2211
BibRef

Wang, X.D.[Xu-Dong], Lian, L.[Long], Yu, S.X.[Stella X.],
Unsupervised Selective Labeling for More Effective Semi-supervised Learning,
ECCV22(XXX:427-445).
Springer DOI 2211
BibRef

Duan, Y.[Yue], Qi, L.[Lei], Wang, L.[Lei], Zhou, L.P.[Lu-Ping], Shi, Y.[Yinghuan],
RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning,
ECCV22(XXX:533-549).
Springer DOI 2211
BibRef

Kim, J.[Jiwon], Min, Y.[Youngjo], Kim, D.[Daehwan], Lee, G.[Gyuseong], Seo, J.[Junyoung], Ryoo, K.[Kwangrok], Kim, S.[Seungryong],
ConMatch: Semi-supervised Learning with Confidence-Guided Consistency Regularization,
ECCV22(XXX:674-690).
Springer DOI 2211
BibRef

He, X.L.[Xin-Lei], Liu, H.B.[Hong-Bin], Gong, N.Z.Q.[Neil Zhen-Qiang], Zhang, Y.[Yang],
Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning,
ECCV22(XXXI:365-381).
Springer DOI 2211
BibRef

Rizve, M.N.[Mamshad Nayeem], Kardan, N.[Navid], Khan, S.[Salman], Khan, F.S.[Fahad Shahbaz], Shah, M.[Mubarak],
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning,
ECCV22(XXXI:382-401).
Springer DOI 2211
BibRef

Tao, C.X.[Chen-Xin], Wang, H.H.[Hong-Hui], Zhu, X.[Xizhou], Dong, J.H.[Jia-Hua], Song, S.[Shiji], Huang, G.[Gao], Dai, J.F.[Ji-Feng],
Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient Framework,
CVPR22(14411-14420)
IEEE DOI 2210
Training, Visualization, Codes, Redundancy, Memory management, Self-supervised learning, Self- semi- meta- unsupervised learning BibRef

Ren, S.[Sucheng], Wang, H.Y.[Hui-Yu], Gao, Z.Q.[Zheng-Qi], He, S.F.[Sheng-Feng], Yuille, A.L.[Alan L.], Zhou, Y.Y.[Yu-Yin], Xie, C.[Cihang],
A Simple Data Mixing Prior for Improving Self-Supervised Learning,
CVPR22(14575-14584)
IEEE DOI 2210
Training, Codes, Self-supervised learning, Transformers, Robustness, Representation learning BibRef

Zhang, T.[Tong], Qiu, C.[Congpei], Ke, W.[Wei], Süsstrunk, S.[Sabine], Salzmann, M.[Mathieu],
Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy,
CVPR22(16559-16568)
IEEE DOI 2210
Representation learning, Codes, Computational modeling, Crops, Self-supervised learning, Feature extraction, Self- semi- meta- unsupervised learning BibRef

Zhang, S.F.[Shao-Feng], Qiu, L.[Lyn], Zhu, F.[Feng], Yan, J.C.[Jun-Chi], Zhang, H.R.[Heng-Rui], Zhao, R.[Rui], Li, H.Y.[Hong-Yang], Yang, X.K.[Xiao-Kang],
Align Representations with Base: A New Approach to Self-Supervised Learning,
CVPR22(16579-16588)
IEEE DOI 2210
Training, Representation learning, Redundancy, Self-supervised learning, Linear programming, Complexity theory, Self- semi- meta- unsupervised learning BibRef

Kim, N.R.[Noo-Ri], Lee, J.H.[Jee-Hyong],
Propagation Regularizer for Semi-supervised Learning with Extremely Scarce Labeled Samples,
CVPR22(14381-14390)
IEEE DOI 2210
Degradation, Costs, Computational modeling, Semisupervised learning, Stability analysis, Data models, Self- semi- meta- unsupervised learning BibRef

Zheng, M.K.[Ming-Kai], You, S.[Shan], Huang, L.[Lang], Wang, F.[Fei], Qian, C.[Chen], Xu, C.[Chang],
SimMatch: Semi-supervised Learning with Similarity Matching,
CVPR22(14451-14461)
IEEE DOI 2210
Training, Annotations, Semantics, Machine learning, Semisupervised learning, Benchmark testing, Self- semi- meta- unsupervised learning BibRef

He, R.D.[Run-Dong], Han, Z.Y.[Zhong-Yi], Lu, X.K.[Xian-Kai], Yin, Y.L.[Yi-Long],
Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class Unlabeled Data,
CVPR22(14565-14574)
IEEE DOI 2210
Perturbation methods, Semisupervised learning, Performance gain, Robustness, Iterative methods, Representation learning BibRef

Tang, H.[Hui], Jia, K.[Kui],
Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning,
CVPR22(14638-14647)
IEEE DOI 2210
Training, Semantics, Prototypes, Semisupervised learning, Information filters, Taylor series, Synchronization, Representation learning BibRef

Hou, Z.J.[Ze-Jiang], Kung, S.Y.[Sun-Yuan],
Semi-Supervised Few-Shot Learning from A Dependency-Discriminant Perspective,
ECV22(2816-2824)
IEEE DOI 2210
Training, Training data, Benchmark testing, Data models BibRef

Lee, D.[Doyup], Kim, S.[Sungwoong], Kim, I.[Ildoo], Cheon, Y.[Yeongjae], Cho, M.[Minsu], Han, W.S.[Wook-Shin],
Contrastive Regularization for Semi-Supervised Learning,
L3D-IVU22(3910-3919)
IEEE DOI 2210
Training, Analytical models, Memory management, Semisupervised learning, Benchmark testing BibRef

Banitalebi-Dehkordi, A.[Amin], Gujjar, P.[Pratik], Zhang, Y.[Yong],
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data,
L3D-IVU22(3998-4005)
IEEE DOI 2210
Degradation, Self-supervised learning, Semisupervised learning, Prediction algorithms, Entropy BibRef

Lai, Z.F.[Zheng-Feng], Wang, C.[Chao], Cheung, S.C.[Sen-Ching], Chuah, C.N.[Chen-Nee],
SaR: Self-adaptive Refinement on Pseudo Labels for Multiclass-Imbalanced Semi-supervised Learning,
L3D-IVU22(4090-4099)
IEEE DOI 2210
Training, Semisupervised learning, Classification algorithms, Task analysis BibRef

Zhang, Y.H.[Yu-Hang], Zhang, X.P.[Xiao-Peng], Xie, L.X.[Ling-Xi], Li, J.[Jie], Qiu, R.C.[Robert C.], Hu, H.T.[Heng-Tong], Tian, Q.[Qi],
One-bit Active Query with Contrastive Pairs,
CVPR22(9687-9695)
IEEE DOI 2210
Measurement, Learning systems, Training, Uncertainty, Costs, Predictive models, Benchmark testing, Self- semi- meta- Others, Representation learning BibRef

Ganeshan, A.[Aditya], Vallet, A.[Alexis], Kudo, Y.[Yasunori], Maeda, S.I.[Shin-Ichi], Kerola, T.[Tommi], Ambrus, R.[Rares], Park, D.[Dennis], Gaidon, A.[Adrien],
Warp-Refine Propagation: Semi-Supervised Auto-Labeling via Cycle-Consistency,
ICCV21(15479-15489)
IEEE DOI 2203
Training, Deep learning, Image segmentation, Annotations, Semantics, Video sequences, Scene analysis and understanding, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Li, J.N.[Jun-Nan], Xiong, C.M.[Cai-Ming], Hoi, S.C.H.[Steven C. H.],
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization,
ICCV21(9455-9464)
IEEE DOI 2203
Representation learning, Codes, Supervised learning, Training data, Semisupervised learning, Task analysis, Representation learning BibRef

Xu, H.M.[Hai-Ming], Liu, L.Q.[Ling-Qiao], Gong, D.[Dong],
Semi-supervised Learning via Conditional Rotation Angle Estimation,
DICTA21(01-08)
IEEE DOI 2201
Couplings, Annotations, Digital images, Estimation, Semisupervised learning, Predictive models, Task analysis BibRef

Wang, W.J.[Wen-Jing], Lin, L.[Lilang], Fan, Z.[Zejia], Liu, J.Y.[Jia-Ying],
Semi-Supervised Learning for Mars Imagery Classification,
ICIP21(499-503)
IEEE DOI 2201
Space vehicles, Mars, Strips, Image processing, Redundancy, Semisupervised learning, Mars image, classification, unsupervised learning BibRef

Taccari, L.[Leonardo],
Domain-Based Semi-Supervised Learning: Exploiting Label Invariance in Unlabeled Data from Distributed Cameras,
DSC21(290-297)
IEEE DOI 2112
Image segmentation, Connected vehicles, Time series analysis, Supervised learning, Semantics, Distributed databases, Semisupervised learning BibRef

Pham, H.[Hieu], Dai, Z.H.[Zi-Hang], Xie, Q.Z.[Qi-Zhe], Le, Q.V.[Quoc V.],
Meta Pseudo Labels,
CVPR21(11552-11563)
IEEE DOI 2111
Semisupervised learning, Benchmark testing, Standards BibRef

Nassar, I.[Islam], Herath, S.[Samitha], Abbasnejad, E.[Ehsan], Buntine, W.[Wray], Haffari, G.[Gholamreza],
All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training,
CVPR21(7237-7246)
IEEE DOI 2111
Training, Visualization, Semantics, Semisupervised learning, Predictive models, Labeling BibRef

Taherkhani, F.[Fariborz], Dabouei, A.[Ali], Soleymani, S.[Sobhan], Dawson, J.[Jeremy], Nasrabadi, N.M.[Nasser M.],
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification,
CVPR21(12262-12272)
IEEE DOI 2111
Measurement, Training, Clustering algorithms, Semisupervised learning, Data models, Convolutional neural networks BibRef

Hu, Z.J.[Zi-Jian], Yang, Z.Y.[Zheng-Yu], Hu, X.F.[Xue-Feng], Nevatia, R.[Ram],
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification,
CVPR21(15094-15103)
IEEE DOI 2111
Training, Codes, Computational modeling, Transfer learning, Performance gain, Semisupervised learning BibRef

Cai, Z.W.[Zhao-Wei], Ravichandran, A.[Avinash], Maji, S.[Subhransu], Fowlkes, C.[Charless], Tu, Z.W.[Zhuo-Wen], Soatto, S.[Stefano],
Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning,
CVPR21(194-203)
IEEE DOI 2111
Training, Codes, Semisupervised learning, Network architecture, Standards BibRef

Groenendijk, R.[Rick], Karaoglu, S.[Sezer], Gevers, T.[Theo], Mensink, T.[Thomas],
Multi-Loss Weighting with Coefficient of Variations,
WACV21(1468-1477)
IEEE DOI 2106
Training, Weight measurement, Uncertainty, Semantics, Estimation, Loss measurement BibRef

Patro, B.N.[Badri N.], Kasturi, G.S., Jain, A.[Ansh], Namboodiri, V.P.[Vinay P.],
Self Supervision for Attention Networks,
WACV21(726-735)
IEEE DOI 2106
Deep learning, Visualization, Correlation, Text categorization, Semantics, Predictive models BibRef

Jing, L.L.[Long-Long], Parag, T.[Toufiq], Wu, Z.[Zhe], Tian, Y.L.[Ying-Li], Wang, H.C.[Hong-Cheng],
VideoSSL: Semi-Supervised Learning for Video Classification,
WACV21(1109-1118)
IEEE DOI 2106
Training, Semisupervised learning, Robustness, Classification algorithms BibRef

Zhu, J.G.[Jia-Geng], Xie, H.C.[Han-Chen], Abd-Almageed, W.[Wael],
Weakly Supervised Invariant Representation Learning via Disentangling Known and Unknown Nuisance Factors,
Scarce22(382-395).
Springer DOI 2304
BibRef

Xie, H.C.[Han-Chen], Hussein, M.E.[Mohamed E.], Galstyan, A.[Aram], Abd-Almageed, W.[Wael],
MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization,
WACV21(2585-2594)
IEEE DOI 2106
Training, Neural networks, Training data, Muscles, Semisupervised learning, Entropy, Task analysis BibRef

Chen, Z.X.[Ze-Xi], Dutton, B.[Benjamin], Ramachandra, B.[Bharathkumar], Wu, T.F.[Tian-Fu], Vatsavai, R.R.[Ranga Raju],
Local Clustering with Mean Teacher for Semi-supervised learning,
ICPR21(6243-6250)
IEEE DOI 2105
Training, Perturbation methods, Predictive models, Benchmark testing, Semisupervised learning, Data models BibRef

Ortego, D.[Diego], Arazo, E.[Eric], Albert, P.[Paul], O'Connor, N.E.[Noel E.], McGuinness, K.[Kevin],
Towards Robust Learning with Different Label Noise Distributions,
ICPR21(7020-7027)
IEEE DOI 2105
Training, Degradation, Semisupervised learning, Robustness, Noise measurement, Labeling BibRef

Haase-Schütz, C.[Christian], Stal, R.[Rainer], Hertlein, H.[Heinz], Sick, B.[Bernhard],
Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning,
ICPR21(9483-9490)
IEEE DOI 2105
Training, Uncertainty, Neural networks, Training data, Semisupervised learning, Network architecture BibRef

Wang, Z.X.[Zi-Xiao], Xu, H.[Hai], Tian, Y.L.[You-Liang], Xie, H.T.[Hong-Tao],
Hierarchical Consistency and Refinement for Semi-Supervised Medical Segmentation,
MMDLCA20(267-276).
Springer DOI 2103
BibRef

Kohli, A.P.S., Sitzmann, V., Wetzstein, G.,
Semantic Implicit Neural Scene Representations With Semi-Supervised Training,
3DV20(423-433)
IEEE DOI 2102
Semantics, Geometry, Image segmentation, Task analysis, Multi modal Representations BibRef

Kang, M.G.[Min-Geun], Lee, K.[Kiwon], Lee, Y.H.[Yong H.], Suh, C.H.[Chang-Ho],
Autoencoder-based Graph Construction for Semi-supervised Learning,
ECCV20(XXIV:500-517).
Springer DOI 2012
BibRef

Chen, Y.C.[Yun-Chun], Chou, C.T.[Chao-Te], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Learning to Learn in a Semi-Supervised Fashion,
ECCV20(XVIII:460-478).
Springer DOI 2012
BibRef

Ke, Z.H.[Zhang-Han], Qiu, D.[Di], Li, K.C.[Kai-Can], Yan, Q.[Qiong], Lau, R.W.H.[Rynson W. H.],
Guided Collaborative Training for Pixel-Wise Semi-Supervised Learning,
ECCV20(XIII:429-445).
Springer DOI 2011
BibRef

Shin, M.C.[Min-Chul],
Semi-supervised Learning with a Teacher-student Network for Generalized Attribute Prediction,
ECCV20(XI:509-525).
Springer DOI 2011
BibRef

Cheng, L.[Lele], Zhou, X.Z.[Xiang-Zeng], Zhao, L.M.[Li-Ming], Li, D.W.[Dang-Wei], Shang, H.[Hong], Zheng, Y.[Yun], Pan, P.[Pan], Xu, Y.H.[Ying-Hui],
Weakly Supervised Learning with Side Information for Noisy Labeled Images,
ECCV20(XXX: 306-321).
Springer DOI 2010
BibRef

Lin, W.Y.[Wan-Yu], Gao, Z.L.[Zhao-Lin], Li, B.C.[Bao-Chun],
Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data,
CVPR20(4173-4181)
IEEE DOI 2008
Semisupervised learning, Measurement, Task analysis, Semantics, Supervised learning, Laplace equations, Machine learning BibRef

Chen, P., Ma, T., Qin, X., Xu, W., Zhou, S.,
Data-Efficient Semi-Supervised Learning by Reliable Edge Mining,
CVPR20(9189-9198)
IEEE DOI 2008
Reliability, Training, Feature extraction, Task analysis, Data mining, Entropy, Semisupervised learning BibRef

Lokhande, V.S., Tasneeyapant, S., Venkatesh, A., Ravi, S.N., Singh, V.,
Generating Accurate Pseudo-Labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations,
CVPR20(11432-11440)
IEEE DOI 2008
Convergence, Optimization, Neural networks, Training, Semisupervised learning, Supervised learning BibRef

Li, S., Liu, B., Chen, D., Chu, Q., Yuan, L., Yu, N.,
Density-Aware Graph for Deep Semi-Supervised Visual Recognition,
CVPR20(13397-13406)
IEEE DOI 2008
Feature extraction, Training, Visualization, Semisupervised learning, Predictive models, Aggregates BibRef

Rebuffi, S.A.[Sylvestre-Alvise], Ehrhardt, S.[Sebastien], Han, K.[Kai], Vedaldi, A.[Andrea], Zisserman, A.[Andrew],
LSD-C: Linearly Separable Deep Clusters,
VIPriors21(1038-1046)
IEEE DOI 2112
BibRef
Earlier:
Semi-Supervised Learning with Scarce Annotations,
DeepVision20(3294-3302)
IEEE DOI 2008
Measurement, Codes, Clustering methods, Clustering algorithms, Benchmark testing. Data models, Task analysis, Training, Perturbation methods, Neural networks, Optimization, Predictive models BibRef

Ke, Z., Wang, D., Yan, Q., Ren, J., Lau, R.,
Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning,
ICCV19(6727-6735)
IEEE DOI 2004
convolutional neural nets, image classification, moving average processes, supervised learning, Dual Student, Benchmark testing BibRef

Beyer, L., Zhai, X., Oliver, A., Kolesnikov, A.,
S4L: Self-Supervised Semi-Supervised Learning,
ICCV19(1476-1485)
IEEE DOI 2004
image classification, image representation, supervised learning, semisupervised ILSVRC-2012, S4L, semisupervised learning, Benchmark testing BibRef

Wang, Q., Li, W., Van Gool, L.J.,
Semi-Supervised Learning by Augmented Distribution Alignment,
ICCV19(1466-1475)
IEEE DOI 2004
Code, Learning.
WWW Link. interpolation, neural nets, supervised learning, semisupervised learning approach, unlabeled data, Benchmark testing BibRef

Cicek, S., Soatto, S.,
Input and Weight Space Smoothing for Semi-Supervised Learning,
MDALC19(1344-1353)
IEEE DOI 2004
gradient methods, learning (artificial intelligence), minimax techniques, ABCD, weight-space smoothing, gradient ascent, Deep learning BibRef

Wu, S.[Si], Li, J.[Jichang], Liu, C.[Cheng], Yu, Z.W.[Zhi-Wen], Wong, H.S.[Hau-San],
Mutual Learning of Complementary Networks via Residual Correction for Improving Semi-Supervised Classification,
CVPR19(6493-6502).
IEEE DOI 2002
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Li, Q.[Qimai], Wu, X.M.[Xiao-Ming], Liu, H.[Han], Zhang, X.T.[Xiao-Tong], Guan, Z.C.[Zhi-Chao],
Label Efficient Semi-Supervised Learning via Graph Filtering,
CVPR19(9574-9583).
IEEE DOI 2002
BibRef

Wang, S.[Suchen], Meng, J.J.[Jing-Jing], Yuan, J.S.[Jun-Song], Tan, Y.P.[Yap-Peng],
Joint Representative Selection and Feature Learning: A Semi-Supervised Approach,
CVPR19(5998-6006).
IEEE DOI 2002
BibRef

Iscen, A.[Ahmet], Tolias, G.[Giorgos], Avrithis, Y.[Yannis], Chum, O.[Ondrej],
Label Propagation for Deep Semi-Supervised Learning,
CVPR19(5065-5074).
IEEE DOI 2002
BibRef

Jiang, B.[Bo], Lin, D.D.[Dou-Dou], Tang, J.[Jin], Luo, B.[Bin],
Data Representation and Learning With Graph Diffusion-Embedding Networks,
CVPR19(10406-10415).
IEEE DOI 2002
BibRef

Jiang, B.[Bo], Zhang, Z.Y.[Zi-Yan], Lin, D.D.[Dou-Dou], Tang, J.[Jin], Luo, B.[Bin],
Semi-Supervised Learning With Graph Learning-Convolutional Networks,
CVPR19(11305-11312).
IEEE DOI 2002
BibRef

Dornaika, F., Bosaghzadeh, A.,
Adaptive Hybrid Representation for Graph-Based Semi-supervised Classification,
CIAP19(I:164-174).
Springer DOI 1909
BibRef

Zhang, H.[Huan], Zhang, Z.[Zhao], Li, S.[Sheng], Ye, Q.L.[Qiao-Lin], Zhao, M.B.[Ming-Bo], Wang, M.[Meng],
Robust Adaptive Label Propagation by Double Matrix Decomposition,
ICPR18(2160-2165)
IEEE DOI 1812
Matrix decomposition, Data models, Adaptation models, Optimization, Sparse matrices, Predictive models, Field-flow fractionation, clean space BibRef

Radosavovic, I.[Ilija], Dollár, P.[Piotr], Girshick, R.[Ross], Gkioxari, G.[Georgia], He, K.M.[Kai-Ming],
Data Distillation: Towards Omni-Supervised Learning,
CVPR18(4119-4128)
IEEE DOI 1812
Data models, Predictive models, Training, Semisupervised learning, Transforms, Head, Heating systems BibRef

Luo, Y., Zhu, J., Li, M., Ren, Y., Zhang, B.,
Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning,
CVPR18(8896-8905)
IEEE DOI 1812
Perturbation methods, Training, Predictive models, Manifolds, Semisupervised learning, Data models BibRef

Chen, L., Yu, S., Yang, M.,
Semi-supervised convolutional neural networks with label propagation for image classification,
ICPR18(1319-1324)
IEEE DOI 1812
Training, Estimation, Training data, Convolutional neural networks, Convolution, semi-supervised learning, label propagation, convolutiona neural network BibRef

Zhu, H., Xia, M.,
Scalable Semi-Supervised Learning by Graph Construction with Approximate Anchors Embedding,
ICPR18(1331-1336)
IEEE DOI 1812
Machine learning, Semisupervised learning, Supervised learning, Time complexity, Laplace equations, Training data, Vector quantization BibRef

Hailat, Z., Komarichev, A., Chen, X.,
Deep Semi-Supervised Learning,
ICPR18(2154-2159)
IEEE DOI 1812
convolution, feedforward neural nets, learning (artificial intelligence), pattern classification, Neural networks BibRef

Park, D.H., Hendricks, L.A., Akata, Z., Rohrbach, A., Schiele, B., Darrell, T.J., Rohrbach, M.,
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence,
CVPR18(8779-8788)
IEEE DOI 1812
Visualization, Task analysis, Activity recognition, Image segmentation, Knowledge discovery, Predictive models BibRef

Gao, M.F.[Ming-Fei], Li, A.[Ang], Yu, R.[Ruichi], Morariu, V.I.[Vlad I.], Davis, L.S.[Larry S.],
C-WSL: Count-Guided Weakly Supervised Localization,
ECCV18(I: 155-171).
Springer DOI 1810
BibRef

Cicek, S.[Safa], Fawzi, A.[Alhussein], Soatto, S.[Stefano],
SaaS: Speed as a Supervisor for Semi-supervised Learning,
ECCV18(II: 152-166).
Springer DOI 1810
BibRef

Mahajan, D.[Dhruv], Girshick, R.[Ross], Ramanathan, V.[Vignesh], He, K.M.[Kai-Ming], Paluri, M.[Manohar], Li, Y.X.[Yi-Xuan], Bharambe, A.[Ashwin], van der Maaten, L.[Laurens],
Exploring the Limits of Weakly Supervised Pretraining,
ECCV18(II: 185-201).
Springer DOI 1810
BibRef

Qiao, S.Y.[Si-Yuan], Shen, W.[Wei], Zhang, Z.S.[Zhi-Shuai], Wang, B.[Bo], Yuille, A.L.[Alan L.],
Deep Co-Training for Semi-Supervised Image Recognition,
ECCV18(XV: 142-159).
Springer DOI 1810
BibRef

Odate, R.[Ryosuke], Shinjo, H.[Hiroshi], Suzuki, Y.[Yasufumi], Motobayashi, M.[Masahiro],
Semi-supervised Clustering Framework Based on Active Learning for Real Data,
SSSPR18(184-193).
Springer DOI 1810
BibRef

Chen, Y.B.[Yan-Bei], Zhu, X.T.[Xia-Tian], Gong, S.G.[Shao-Gang],
Semi-supervised Deep Learning with Memory,
ECCV18(I: 275-291).
Springer DOI 1810
BibRef

Shi, W.W.[Wei-Wei], Gong, Y.H.[Yi-Hong], Ding, C.[Chris], Ma, Z.H.[Zhi-Heng], Tao, X.Y.[Xiao-Yu], Zheng, N.N.[Nan-Ning],
Transductive Semi-Supervised Deep Learning Using Min-Max Features,
ECCV18(VI: 311-327).
Springer DOI 1810
BibRef

Liu, Y.[Yu], Song, G.L.[Guang-Lu], Shao, J.[Jing], Jin, X.[Xiao], Wang, X.G.[Xiao-Gang],
Transductive Centroid Projection for Semi-supervised Large-Scale Recognition,
ECCV18(VI: 72-89).
Springer DOI 1810
BibRef

Robert, T.[Thomas], Thome, N.[Nicolas], Cord, M.[Matthieu],
HybridNet: Classification and Reconstruction Cooperation for Semi-supervised Learning,
ECCV18(VII: 158-175).
Springer DOI 1810
BibRef

Chen, X., Gong, C., Ma, C., Huang, X., Yang, J.,
Privileged Semi-Supervised Learning,
ICIP18(2999-3003)
IEEE DOI 1809
Training, Support vector machines, Manifolds, Semisupervised learning, Kernel, Standards, Feature extraction, support vector machine BibRef

Zhang, L., Wang, D., Zhang, X., Gu, N., Fan, M.,
Simultaneous Learning of Affinity Matrix and Laplacian Regularized Least Squares for Semi-Supervised Classification,
ICIP18(1633-1637)
IEEE DOI 1809
Sparse matrices, Laplace equations, Optimization, Semisupervised learning, Benchmark testing, Manifolds, Laplacian Regularized Least Squares BibRef

Ding, Y.F.[Yi-Fan], Wang, L.Q.[Li-Qiang], Fan, D.L.[De-Liang], Gong, B.Q.[Bo-Qing],
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels,
WACV18(1215-1224)
IEEE DOI 1806
image classification, learning (artificial intelligence), neural nets, probability, data points, inferred noisy rates, Training data BibRef

Huijser, M.[Miriam], van Gemert, J.C.[Jan C.],
Active Decision Boundary Annotation with Deep Generative Models,
ICCV17(5296-5305)
IEEE DOI 1802
Choose where on a 1D scale to put the boundary. Interactive learning. image annotation, learning (artificial intelligence), active decision boundary annotation, active learning methods, Visualization BibRef

Gaur, U., Manjunath, B.S.,
Weakly Supervised Manifold Learning for Dense Semantic Object Correspondence,
ICCV17(1744-1752)
IEEE DOI 1802
feature extraction, feedforward neural nets, geometry, image classification, learning (artificial intelligence), Semantics BibRef

Jacobs, M.[Matt],
A Fast MBO Scheme for Multiclass Data Classification,
SSVM17(335-347).
Springer DOI 1706
semi-supervised data classification BibRef

Salinas-Gutiérrez, R.[Rogelio], Hernández-Quintero, A.[Angélica], Dalmau-Cedeño, O.[Oscar], Pérez-Díaz, Á.P.[Ángela Paulina],
Modeling Dependencies in Supervised Classification,
MCPR17(117-126).
Springer DOI 1706
BibRef

Valev, V., Yanev, N., Krzyzak, A.,
A new geometrical approach for solving the supervised pattern recognition problem,
ICPR16(1648-1652)
IEEE DOI 1705
Color, Complexity theory, Optimization, Partitioning algorithms, Training BibRef

Hou, C.Q.[Cui-Qin], Xia, Y.J.[Ying-Ju], Xu, Z.R.[Zhuo-Ran], Sun, J.[Jun],
Semi-supervised learning competence of classifiers based on graph for dynamic classifier selection,
ICPR16(3650-3654)
IEEE DOI 1705
Classification algorithms, Heuristic algorithms, Optimization, Testing, Training, Training data, competence, dynamic classifier selection, ensemble learning, graph, semi-supervised BibRef

Oh, B.[Byonghwa], Yang, J.[Jihoon],
Enhancing label inference algorithms considering vertex importance in graph-based semi-supervised learning,
ICPR16(1671-1676)
IEEE DOI 1705
Algorithm design and analysis, Convergence, Heuristic algorithms, Inference algorithms, Manifolds, Prediction algorithms, Supervised, learning BibRef

Sousa, C.A.R.[Celso A. R.], Batista, G.E.A.P.A.[Gustavo E.A.P.A.],
Constrained Local and Global Consistency for semi-supervised learning,
ICPR16(1689-1694)
IEEE DOI 1705
Benchmark testing, Closed-form solutions, Convex functions, Laplace equations, Optimization, Robustness, Semisupervised, learning BibRef

Fu, Y.W.[Yan-Wei], Sigal, L.[Leonid],
Semi-supervised Vocabulary-Informed Learning,
CVPR16(5337-5346)
IEEE DOI 1612
BibRef

Krijthe, J.H.[Jesse H.], Loog, M.[Marco],
The Peaking Phenomenon in Semi-supervised Learning,
SSSPR16(299-309).
Springer DOI 1611
BibRef

Kim, K.I.[Kwang In],
Semi-supervised Learning Based on Joint Diffusion of Graph Functions and Laplacians,
ECCV16(V: 713-729).
Springer DOI 1611
BibRef

Li, A., Jabri, A.[Allan], Joulin, A.[Armand], van der Maaten, L.[Laurens],
Learning Visual N-Grams from Web Data,
ICCV17(4193-4202)
IEEE DOI 1802
convolution, image annotation, image recognition, image retrieval, learning (artificial intelligence), neural nets, Visualization BibRef

Joulin, A.[Armand], van der Maaten, L.[Laurens], Jabri, A.[Allan], Vasilache, N.[Nicolas],
Learning Visual Features from Large Weakly Supervised Data,
ECCV16(VII: 67-84).
Springer DOI 1611
BibRef

Gangeh, M.J.[Mehrdad J.], Bedawi, S.M.A.[Safaa M. A.], Ghodsi, A.[Ali], Karray, F.[Fakhri],
Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion,
ICIAR16(12-19).
Springer DOI 1608
BibRef

Li, C.G.[Chun-Guang], Lin, Z.C.[Zhou-Chen], Zhang, H.G.[Hong-Gang], Guo, J.[Jun],
Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning,
ICCV15(2767-2775)
IEEE DOI 1602
BibRef

Wang, X.B.[Xiao-Bo], Guo, X.J.[Xiao-Jie], Li, S.Z.[Stan Z.],
Adaptively Unified Semi-Supervised Dictionary Learning with Active Points,
ICCV15(1787-1795)
IEEE DOI 1602
Data models BibRef

Shi, X.[Xin], Zhang, C.[Chao], Wei, F.Y.[Fang-Yun], Zhang, H.Y.[Hong-Yang], She, Y.Y.[Yi-Yuan],
Manifold-Regularized Selectable Factor Extraction for Semi-supervised Image Classification,
BMVC15(xx-yy).
DOI Link 1601
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Yin, Q.Y.[Qi-Yue], Wu, S.[Shu], Wang, L.[Liang],
Partially tagged image clustering,
ICIP15(4012-4016)
IEEE DOI 1512
Image clustering BibRef

Yang, H.X.[Hong-Xue], Kong, X.W.[Xiang-Wei], Fu, H.Y.[Hai-Yan], Li, M.[Ming], Zhao, G.P.[Gen-Ping],
Semi-supervised learning based on group sparse for relative attributes,
ICIP15(3931-3935)
IEEE DOI 1512
Group sparse; labeling; relative attributes; semi-supervised learning BibRef

Ye, Z.P.[Zhi-Peng], Liu, P.[Peng], Tang, X.L.[Xiang-Long], Zhao, W.[Wei],
May the torcher light our way: A negative-accelerated active learning framework for image classification,
ICIP15(1658-1662)
IEEE DOI 1512
Image classification BibRef

Cardona, H.D.V.[Hernán Darío Vargas], Álvarez, M.A.[Mauricio A.], Orozco, Á.A.[Álvaro A.],
Convolved Multi-output Gaussian Processes for Semi-Supervised Learning,
CIAP15(I:109-118).
Springer DOI 1511
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Gómez-González, S.[Sebastián], Álvarez, M.A.[Mauricio A.], García, H.F.[Hernan F.], Ríos, J.I.[Jorge I.], Orozco, A.A.[Alvaro A.],
Discriminative Training for Convolved Multiple-Output Gaussian Processes,
CIARP15(595-602).
Springer DOI 1511
BibRef

Peikari, M.[Mohammad], Zubovits, J.[Judit], Clarke, G.[Gina], Martel, A.L.[Anne L.],
Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology,
MLMI15(263-270).
Springer DOI 1511
BibRef

Wang, Z.H.[Zi-Heng], Ji, Q.A.[Qi-Ang],
Classifier learning with hidden information,
CVPR15(4969-4977)
IEEE DOI 1510
BibRef

Kim, K.I.[Kwang In], Tompkin, J.[James], Pfister, H.[Hanspeter], Theobalt, C.[Christian],
Semi-supervised learning with explicit relationship regularization,
CVPR15(2188-2196)
IEEE DOI 1510
BibRef

Wuttke, S., Middelmann, W., Stilla, U.,
Concept for a Compound Analysis in Active Learning for Remote Sensing,
PIA15(273-279).
DOI Link 1504
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Pham, H.D.[Hien Duy], Kim, K.H.[Kye-Hyeon], Choi, S.J.[Seung-Jin],
Semi-supervised Learning on Bi-relational Graph for Image Annotation,
ICPR14(2465-2470)
IEEE DOI 1412
Computational modeling BibRef

Berton, L.[Lilian], de Andrade Lopes, A.[Alneu],
Graph Construction Based on Labeled Instances for Semi-supervised Learning,
ICPR14(2477-2482)
IEEE DOI 1412
Accuracy BibRef

Yang, Y.[Yi], Newsam, S.[Shawn],
Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration,
ICPR14(4062-4067)
IEEE DOI 1412
Computational modeling BibRef

Aodha, O.M.[Oisin Mac], Campbell, N.D.F.[Neill D.F.], Kautz, J.[Jan], Brostow, G.J.[Gabriel J.],
Hierarchical Subquery Evaluation for Active Learning on a Graph,
CVPR14(564-571)
IEEE DOI 1409
active learning; semi-supervised learning BibRef

Lai, H.J.[Han-Jiang], Pan, Y.[Yan], Lu, C.Y.[Can-Yi], Tang, Y.[Yong], Yan, S.C.[Shui-Cheng],
Efficient k-Support Matrix Pursuit,
ECCV14(II: 617-631).
Springer DOI 1408
semi-supervised classification BibRef

Lad, S.[Shrenik], Parikh, D.[Devi],
Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations,
ECCV14(VI: 333-349).
Springer DOI 1408
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Wigness, M.[Maggie], Draper, B.A.[Bruce A.], Beveridge, J.R.[J. Ross],
Selectively guiding visual concept discovery,
WACV14(247-254)
IEEE DOI 1406
Selective Guidance. Give some guidance, cluster to find concepts. Accuracy BibRef

Xu, J.S.[Jing-Song], Wu, Q.A.[Qi-Ang], Zhang, J.[Jian], Shen, F.M.[Fu-Min], Tang, Z.M.[Zhen-Min],
Training boosting-like algorithms with semi-supervised subspace learning,
ICIP13(4302-4306)
IEEE DOI 1402
AdaBoost BibRef

Xie, W.X.[Wen-Xuan], Lu, Z.W.[Zhi-Wu], Peng, Y.X.[Yu-Xin], Xiao, J.G.[Jian-Guo],
Multimodal semi-supervised image classification by combining tag refinement, graph-based learning and support vector regression,
ICIP13(4307-4311)
IEEE DOI 1402
Graph-based semi-supervised learning BibRef

Hoai, M.[Minh], Zisserman, A.[Andrew],
Discriminative Sub-categorization,
CVPR13(1666-1673)
IEEE DOI 1309
Weakly supervised with positive and negative examples. BibRef

Asafi, S.[Shmuel], Cohen-Or, D.[Daniel],
Constraints as Features,
CVPR13(1634-1641)
IEEE DOI 1309
clustering; image segmentation; machine learning; semi-supervised Constrained clustering using cannot-link information. BibRef

Turakhia, N.[Naman], Parikh, D.[Devi],
Attribute Dominance: What Pops Out?,
ICCV13(1225-1232)
IEEE DOI 1403
attribute based classification BibRef

Biswas, A.[Arijit], Parikh, D.[Devi],
Simultaneous Active Learning of Classifiers: Attributes via Relative Feedback,
CVPR13(644-651)
IEEE DOI 1309
Active Learning. Reduce annotation costs. BibRef

Shi, L.[Lei], Khushaba, R., Kodagoda, S., Dissanayake, G.,
Application of CRF and SVM based semi-supervised learning for semantic labeling of environments,
ICARCV12(835-840).
IEEE DOI 1304
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Ebert, S.[Sandra], Fritz, M.[Mario], Schiele, B.[Bernt],
Semi-Supervised Learning on a Budget: Scaling Up to Large Datasets,
ACCV12(I:232-245).
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Zhang, T.Z.[Tian-Zhu], Cai, R.[Rui], Li, Z.W.[Zhi-Wei], Zhang, L.[Lei], Lu, H.Q.[Han-Qing],
Hierarchical Object Representations for Visual Recognition via Weakly Supervised Learning,
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Cao, C.[Chen], Chen, S.F.[Shi-Feng], Zou, C.Q.[Chang-Qing], Liu, J.Z.[Jian-Zhuang],
Locating high-density clusters with noisy queries,
ICPR12(3537-3540).
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Hido, S.[Shohei], Kashima, H.[Hisashi],
Hash-based structural similarity for semi-supervised Learning on attribute graphs,
ICPR12(3009-3012).
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Lopresti, D.P.[Daniel P.], Nagy, G.[George],
Optimal data partition for semi-automated labeling,
ICPR12(286-289).
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Kobayashi, T.[Takumi], Otsu, N.[Nobuyuki],
Efficient Similarity Derived from Kernel-Based Transition Probability,
ECCV12(VI: 371-385).
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Siva, P.[Parthipan], Russell, C.[Chris], Xiang, T.[Tao],
In Defence of Negative Mining for Annotating Weakly Labelled Data,
ECCV12(III: 594-608).
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Annotate based on examples that have not occurred previously. BibRef

Shrivastava, A.[Abhinav], Singh, S.[Saurabh], Gupta, A.[Abhinav],
Constrained Semi-Supervised Learning Using Attributes and Comparative Attributes,
ECCV12(III: 369-383).
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Ahumada, H.C.[Hernán C.], Granitto, P.M.[Pablo M.],
A Simple Hybrid Method for Semi-supervised Learning,
CIARP12(138-145).
Springer DOI 1209
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Duvenaud, D.[David], Marlin, B.[Benjamin], Murphy, K.[Kevin],
Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification,
CRV11(371-378).
IEEE DOI 1105
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Kimura, A.[Akisato], Kameoka, H.[Hirokazu], Sugiyama, M.[Masashi], Nakano, T.[Takuho], Maeda, E.[Eisaku], Sakano, H.[Hitoshi], Ishiguro, K.[Katsuhiko],
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations,
ICPR10(2933-2936).
IEEE DOI 1008
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Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei], Ruan, X.[Xiang],
Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition,
ICPR10(3045-3048).
IEEE DOI 1008
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Chandel, A.S.[Arvind Singh], Tiwari, A.[Aruna], Chaudhari, N.S.[Narendra S.],
Constructive Semi-Supervised Classification Algorithm and Its Implement in Data Mining,
PReMI09(62-67).
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Gui, J.[Jie], Huang, D.S.[De-Shuang], You, Z.H.[Zhu-Hong],
An improvement on learning with local and global consistency,
ICPR08(1-4).
IEEE DOI 0812
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Zhang, Z.Y.[Zhen-Yue], Zha, H.Y.[Hong-Yuan], Zhang, M.[Min],
Spectral methods for semi-supervised manifold learning,
CVPR08(1-6).
IEEE DOI 0806
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Gong, Y.C.[Yun-Chao], Chen, C.L.[Chuan-Liang], Tian, Y.J.[Yin-Jie],
Graph-based semi-supervised learning with redundant views,
ICPR08(1-4).
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Korecki, J.N.[John N.], Banfield, R.E.[Robert E.], Hall, L.O.[Lawrence O.], Bowyer, K.W.[Kevin W.], Kegelmeyer, W.P.[W. Philip],
Semi-supervised learning on large complex simulations,
ICPR08(1-4).
IEEE DOI 0812
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Hu, J.Y.[Jian-Ying], Singh, M.[Moninder], Mojsilovic, A.[Aleksandra],
Categorization using semi-supervised clustering,
ICPR08(1-4).
IEEE DOI 0812
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Cheng, Y.B.[Yu-Bo], Cai, Y.P.[Yun-Peng], Sun, Y.J.[Yi-Jun], Li, J.[Jian],
Semi-supervised feature selection under logistic I-RELIEF framework,
ICPR08(1-4).
IEEE DOI 0812
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Xiao, R.[Rui], Shi, P.F.[Peng-Fei],
Semi-supervised marginal discriminant analysis based on QR decomposition,
ICPR08(1-4).
IEEE DOI 0812
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Tahir, M.A.[Muhammad Atif], Smith, J.E.[James E.], Caleb-Solly, P.[Praminda],
A Novel Feature Selection Based Semi-supervised Method for Image Classification,
CVS08(xx-yy).
Springer DOI 0805

See also Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier. BibRef

Batra, D., Sukthankar, R., Chen, T.,
Semi-Supervised Clustering via Learnt Codeword Distances,
BMVC08(xx-yy).
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Marin-Castro, H.[Heidy], Sucar, L.E.[L. Enrique], Morales, E.[Eduardo],
Automatic Image Annotation Using a Semi-supervised Ensemble of Classifiers,
CIARP07(487-495).
Springer DOI 0711
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Didaci, L.[Luca], Fumera, G.[Giorgio], Roli, F.[Fabio],
Analysis of Co-training Algorithm with Very Small Training Sets,
SSSPR12(719-726).
Springer DOI 1211
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Didaci, L.[Luca], Roli, F.[Fabio],
Using Co-training and Self-training in Semi-supervised Multiple Classifier Systems,
SSPR06(522-530).
Springer DOI 0608
BibRef

Duan, R.[Rong], Jiang, W.[Wei], Man, H.[Hong],
Semi-Supervised Image Classification in Likelihood Space,
ICIP06(957-960).
IEEE DOI 0610
BibRef
And:
Robust Adjusted Likelihood Function for Image Analysis,
AIPR06(29-29).
IEEE DOI 0610
BibRef

Song, Y.Q.[Yang-Qiu], Zhang, C.S.[Chang-Shui], Lee, J.G.[Jian-Guo],
Graph Based Multi-class Semi-supervised Learning Using Gaussian Process,
SSPR06(450-458).
Springer DOI 0608
BibRef

Saint-Jean, C., Frelicot, C.,
A robust semi-supervised EM-based clustering algorithm with a reject option,
ICPR02(III: 399-402).
IEEE DOI 0211
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Semi-Supervised Clustering Applied to Hyperspectral Data .