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0812
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1008
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1212
Supervised learning; Error estimation; High dimension; Small sample
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1503
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1701
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1806
Feature selection, Wide datasets, Experimental protocol,
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MugNet: Deep Learning for Hyperspectral Image Classification Using
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1810
Hyperspectral image classification, Deep learning,
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1904
Limited number of examples.
View-based object recognition, Incremental object models, Features persistence
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Sparsity preserving projection, Dimensionality reduction,
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Zhai, Y.K.[Yi-Kui],
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Mai, C.Y.[Chao-Yun],
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IP(30), 2021, pp. 7266-7279.
IEEE DOI
2108
Target recognition, Task analysis, Training,
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Lin, L.[Luyue],
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IEEE DOI
2206
Training, Decoding, Neural networks, Task analysis,
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Integrating Hybrid Pyramid Feature Fusion and Coordinate Attention
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2206
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Adversarial Representation Learning for Hyperspectral Image
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2209
Annotations, Neural networks, Training, Geometry, Deep learning,
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Feng, F.[Fan],
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Low-Rank Constrained Attention-Enhanced Multiple
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Integrated APC-GAN and AttuNet Framework for Automated Pavement Crack
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IEEE DOI
2304
Image segmentation, Training, Generators, Image resolution,
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PR(139), 2023, pp. 109480.
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2304
Few sample learning, Learn to learn, Survey, Few-shot learning, Meta learning
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Capsule Broad Learning System Network for Robust Synthetic Aperture
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Xie, H.T.[Hong-Tao],
DHVT: Dynamic Hybrid Vision Transformer for Small Dataset Recognition,
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IEEE DOI
2503
Head, Transformers, Training, Convolution,
Image recognition, Data models,
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Mougiakakou, S.[Stavroula],
No Data Augmentation? Alternative Regularizations for Effective
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VIPriors23(139-148)
IEEE DOI
2401
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Pang, Z.Q.[Zi-Qi],
Hu, Z.Y.[Zhi-Yuan],
Tokmakov, P.[Pavel],
Wang, Y.X.[Yu-Xiong],
Hebert, M.[Martial],
Unlocking the Full Potential of Small Data with Diverse Supervision,
LLID21(2642-2652)
IEEE DOI
2109
Training, Learning systems, Head, Protocols, Training data,
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Jeon, H.[Hyeonseong],
Han, S.[Siho],
Lee, S.[Sangwon],
Woo, S.S.[Simon S.],
Compensating for the Lack of Extra Training Data by Learning Extra
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ACCV20(VI:532-548).
Springer DOI
2103
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Hurtik, P.[Petr],
Molek, V.[Vojtech],
Perfilieva, I.[Irina],
Novel dimensionality reduction approach for unsupervised learning on
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PR(103), 2020, pp. 107291.
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2005
Unsupervised learning, Dimensionality reduction, PCA,
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Barz, B.,
Denzler, J.,
Deep Learning on Small Datasets without Pre-Training using Cosine
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WACV20(1360-1369)
IEEE DOI
2006
Machine learning, Training, Semantics, Face, Task analysis,
Neural networks, Training data
BibRef
Sandler, M.,
Baccash, J.,
Zhmoginov, A.,
Howard, A.,
Non-Discriminative Data or Weak Model? On the Relative Importance of
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RLQ19(1036-1044)
IEEE DOI
2004
image resolution, neural net architecture,
isometric neural networks, fixed internal resolution,
BibRef
Kaushal, V.,
Iyer, R.,
Kothawade, S.,
Mahadev, R.,
Doctor, K.,
Ramakrishnan, G.,
Learning From Less Data: A Unified Data Subset Selection and Active
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WACV19(1289-1299)
IEEE DOI
1904
learning (artificial intelligence), neural nets,
object detection, object recognition,
Data models
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Zhang, Z.L.[Zong-Liang],
Li, J.[Jonathan],
Guo, Y.L.[Yu-Lan],
Li, X.[Xin],
Lin, Y.B.[Yang-Bin],
Xiao, G.B.[Guo-Bao],
Wang, C.[Cheng],
Robust procedural model fitting with a new geometric similarity
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PR(85), 2019, pp. 120-131.
Elsevier DOI
1810
Complex model fitting, Imperfect point set,
Inverse procedural modeling, Probabilistic program induction,
Few-shot pattern recognition
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Davari, A.[Amir_Abbas],
Christlein, V.[Vincent],
Vesal, S.[Sulaiman],
Maier, A.[Andreas],
Riess, C.[Christian],
GMM Supervectors for Limited Training Data in Hyperspectral Remote
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CAIP17(II: 296-306).
Springer DOI
1708
BibRef
Ustuner, M.,
Sanli, F.B.,
Abdikan, S.,
Balanced Vs Imbalanced Training Data: Classifying Rapideye Data With
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ISPRS16(B7: 379-384).
DOI Link
1610
BibRef
Su, H.[Hang],
Yin, Z.Z.[Zhao-Zheng],
Kanade, T.[Takeo],
Huh, S.[Seungil],
Active sample selection and correction propagation on a
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CVPR15(1975-1983)
IEEE DOI
1510
BibRef
Kadar, I.[Ilan],
Ben-Shahar, O.[Ohad],
Small sample scene categorization from perceptual relations,
CVPR12(2711-2718).
IEEE DOI
1208
BibRef
Singh, M.[Mayank],
Gupta, P.K.,
Mishra, S.[Shailendra],
Automated test data generation for mutation testing using AspectJ
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ICIIP11(1-5).
IEEE DOI
1112
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Xia, X.T.[Xin-Tao],
Zhou, Q.[Qing],
Zhu, J.M.[Jian-Min],
Evaluation for repeatability and reproducibility of information poor
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IASP10(528-531).
IEEE DOI
1004
Incomplete, small sample size.
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Petersen, H.[Henry],
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Reworking Bridging for Use within the Image Domain,
CAIP09(832-839).
Springer DOI
0909
Bridging: from string text classification (Zelikovitz et al.)
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Ricamato, M.T.[Maria Teresa],
Marrocco, C.[Claudio],
Tortorella, F.[Francesco],
MCS-based balancing techniques for skewed classes:
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ICPR08(1-4).
IEEE DOI
0812
Train multiple classifiers using the minority class and part of majority class.
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Na, J.H.[Jin Hee],
Yun, S.M.[Seok Min],
Kim, M.S.[Min-Soo],
Choi, J.Y.[Jin Young],
Relevant pattern selection for subspace learning,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Xu, Y.[Yong],
Zhang, D.[David],
A New Solution Scheme of Unsupervised Locality Preserving Projection
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SSPR08(775-781).
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0812
Small Sample Size problem.
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Morales-Manilla, L.R.[Luis Roberto],
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FS-EX Plus: A New Algorithm for the Calculation of Typical FS-Testor
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CIARP07(380-386).
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0711
Feature Selection.
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Zheng, Y.J.[Yu-Jie],
Yang, J.Y.[Jing-Yu],
Yang, J.[Jian],
Wu, X.J.[Xiao-Jun],
Effective classification image space which can solve small sample size
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ICPR06(II: 861-864).
IEEE DOI
0609
BibRef
Pranckeviciene, E.[Erinija],
Ho, T.K.[Tin Kam],
Somorjai, R.[Ray],
Class Separability in Spaces Reduced By Feature Selection,
ICPR06(III: 254-257).
IEEE DOI
0609
BibRef
Xuan, G.R.[Guo-Rong],
Zhu, X.M.[Xiu-Ming],
Chai, P.Q.[Pei-Qi],
Zhang, Z.P.[Zhen-Ping],
Shi, Y.Q.[Yun Q.],
Fu, D.D.[Dong-Dong],
Feature Selection based on the Bhattacharyya Distance,
ICPR06(III: 1232-1235).
IEEE DOI
0609
BibRef
And:
ICPR06(IV: 957).
IEEE DOI
0609
BibRef
Levi, K.[Kobi],
Fink, M.[Michael],
Weiss, Y.[Yair],
Learning From a Small Number of Training Examples by Exploiting Object
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LCV04(96).
IEEE DOI
0406
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Ulusoy, I.[Ilkay],
Bishop, C.M.[Christopher M.],
Comparison of Generative and Discriminative Techniques for Object
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CLOR06(173-195).
Springer DOI
0711
BibRef
Earlier:
Generative versus Discriminative Methods for Object Recognition,
CVPR05(II: 258-265).
IEEE DOI
0507
BibRef
Salah, A.A.,
Alpaydin, E.,
Incremental mixtures of factor analysers,
ICPR04(I: 276-279).
IEEE DOI
0409
BibRef
Huang, R.[Rui],
Liu, Q.S.[Qing-Shan],
Lu, H.Q.[Han-Qing],
Ma, S.D.[Song-De],
Solving the small sample size problem of LDAf,
ICPR02(III: 29-32).
IEEE DOI
0211
BibRef
Duin, R.P.W.[Robert P.W.],
Relational Discriminant Analysis and its Large Sample Size Problem,
ICPR98(Vol I: 445-449).
IEEE DOI
9808
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Unbalanced Datasets, Imbalanced Sample Sizes, Imbalanced Data, Long-Tailed Data .