14.2.11 Contrastive Learning

Chapter Contents (Back)
Contrastive Learning. Learning. Contrastive learning, which aims at minimizing the distance between positive pairs while maximizing that of negative ones.

Hu, X.[Xiang], Li, T.[Teng], Zhou, T.[Tong], Peng, Y.X.[Yuan-Xi],
Deep Spatial-Spectral Subspace Clustering for Hyperspectral Images Based on Contrastive Learning,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Hassanin, M.[Mohammed], Radwan, I.[Ibrahim], Khan, S.[Salman], Tahtali, M.[Murat],
Learning discriminative representations for multi-label image recognition,
JVCIR(83), 2022, pp. 103448.
Elsevier DOI 2202
Multi-label recognition, Multi-label-contrastive learning, Contrastive representation, Deep learning BibRef

Dave, I.[Ishan], Gupta, R.[Rohit], Rizve, M.N.[Mamshad Nayeem], Shah, M.[Mubarak],
TCLR: Temporal contrastive learning for video representation,
CVIU(219), 2022, pp. 103406.
Elsevier DOI 2205
Self-Supervised Learning, Action Recognition, Video Representation BibRef


Yang, J.W.[Jian-Wei], Bisk, Y.[Yonatan], Gao, J.F.[Jian-Feng],
TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment,
ICCV21(11542-11552)
IEEE DOI 2203
Location awareness, Representation learning, Visualization, Protocols, Pipelines, Benchmark testing, Syntactics, Vision + language BibRef

Zheng, M.K.[Ming-Kai], Wang, F.[Fei], You, S.[Shan], Qian, C.[Chen], Zhang, C.S.[Chang-Shui], Wang, X.G.[Xiao-Gang], Xu, C.[Chang],
Weakly Supervised Contrastive Learning,
ICCV21(10022-10031)
IEEE DOI 2203
Representation learning, Visualization, Head, Transfer learning, Semisupervised learning, Labeling, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Diba, A.[Ali], Sharma, V.[Vivek], Safdari, R.[Reza], Lotfi, D.[Dariush], Sarfraz, M.S.[M. Saquib], Stiefelhagen, R.[Rainer], Van Gool, L.J.[Luc J.],
Vi2CLR: Video and Image for Visual Contrastive Learning of Representation,
ICCV21(1482-1492)
IEEE DOI 2203
Representation learning, Visualization, Image recognition, Pipelines, Transfer learning, Supervised learning, Representation learning BibRef

Zhong, Z.[Zhun], Fini, E.[Enrico], Roy, S.[Subhankar], Luo, Z.M.[Zhi-Ming], Ricci, E.[Elisa], Sebe, N.[Nicu],
Neighborhood Contrastive Learning for Novel Class Discovery,
CVPR21(10862-10870)
IEEE DOI 2111
Aggregates, Feature extraction, Pattern recognition, Task analysis BibRef

Kodama, Y.[Yuto], Wang, Y.[Yinan], Kawakami, R.[Rei], Naemura, T.[Takeshi],
Open-set Recognition with Supervised Contrastive Learning,
MVA21(1-5)
DOI Link 2109
Training, Computer aided instruction, Training data, Feature extraction, Extraterrestrial measurements, Task analysis BibRef

Ghosh, A.[Aritra], Lan, A.[Andrew],
Contrastive Learning Improves Model Robustness Under Label Noise,
LLID21(2697-2702)
IEEE DOI 2109
Training, Visualization, Training data, Semisupervised learning, Robustness, Pattern recognition BibRef

Rai, N.[Nishant], Adeli, E.[Ehsan], Lee, K.H.[Kuan-Hui], Gaidon, A.[Adrien], Niebles, J.C.[Juan Carlos],
CoCon: Cooperative-Contrastive Learning,
HVU21(3379-3388)
IEEE DOI 2109
Visualization, Semantics, Performance gain, Pattern recognition, Noise measurement, Labeling BibRef

Xie, E.[Enze], Ding, J.[Jian], Wang, W.H.[Wen-Hai], Zhan, X.H.[Xiao-Hang], Xu, H.[Hang], Sun, P.[Peize], Li, Z.G.[Zhen-Guo], Luo, P.[Ping],
DetCo: Unsupervised Contrastive Learning for Object Detection,
ICCV21(8372-8381)
IEEE DOI 2203
Object detection, Detectors, Feature extraction, Task analysis, Image classification, Detection and localization in 2D and 3D BibRef

Wang, X.L.[Xin-Long], Zhang, R.F.[Ru-Feng], Shen, C.H.[Chun-Hua], Kong, T.[Tao], Li, L.[Lei],
Dense Contrastive Learning for Self-Supervised Visual Pre-Training,
CVPR21(3023-3032)
IEEE DOI 2111
Learning systems, Image segmentation, Visualization, Computational modeling, Semantics, Object detection BibRef

Wang, P.[Peng], Han, K.[Kai], Wei, X.S.[Xiu-Shen], Zhang, L.[Lei], Wang, L.[Lei],
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification,
CVPR21(943-952)
IEEE DOI 2111
Network structure being composed of a supervised contrastive loss to learn image representations and a cross-entropy loss to learn classifiers. Training, Memory management, Graphics processing units, Image representation, Proposals BibRef

Kuang, H.F.[Hao-Fei], Zhu, Y.[Yi], Zhang, Z.[Zhi], Li, X.Y.[Xin-Yu], Tighe, J.[Joseph], Schwertfeger, S.[Sören], Stachniss, C.[Cyrill], Li, M.[Mu],
Video Contrastive Learning with Global Context,
CVEU21(3188-3188)
IEEE DOI 2112
Training, Location awareness, Learning systems, Visualization, Conferences BibRef

Lee, K.S.[Kwot Sin], Tran, N.T.[Ngoc-Trung], Cheung, N.M.[Ngai-Man],
InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning,
WACV21(3941-3951)
IEEE DOI 2106
Training, Image synthesis, Generative adversarial networks, Reproducibility of results, Libraries, Generators, Tuning BibRef

Shao, H.[Huan], Yuan, Z.Q.[Zhao-Quan], Peng, X.[Xiao], Wu, X.[Xiao],
Contrastive Learning in Frequency Domain for Non-I.I.D. Image Classification,
MMMod21(I:111-122).
Springer DOI 2106
Not Independent and Identically Distributed. BibRef

Zhu, R.[Rui], Zhao, B.C.[Bing-Chen], Liu, J.G.[Jin-Gen], Sun, Z.L.[Zheng-Long], Chen, C.W.[Chang Wen],
Improving Contrastive Learning by Visualizing Feature Transformation,
ICCV21(10286-10295)
IEEE DOI 2203
Contrastive learning, which aims at minimizing the distance between positive pairs while maximizing that of negative ones. Training, Representation learning, Interpolation, Extrapolation, Visualization, Computational modeling, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Yüksel, O.K.[Oguz Kaan], Simsar, E.[Enis], Er, E.G.[Ezgi Gülperi], Yanardag, P.[Pinar],
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions,
ICCV21(14243-14252)
IEEE DOI 2203
Image synthesis, Annotations, Computational modeling, Semantics, Manuals, Aerospace electronics, Image and video synthesis, Neural generative models BibRef

Kinakh, V.[Vitaliy], Taran, O.[Olga], Voloshynovskiy, S.[Svyatoslav],
ScatSimCLR: Self-Supervised Contrastive Learning with Pretext Task Regularization for Small-Scale Datasets,
VIPriors21(1098-1106)
IEEE DOI 2112
Training, Adaptation models, Computational modeling, Estimation BibRef

Dwibedi, D.[Debidatta], Aytar, Y.[Yusuf], Tompson, J.[Jonathan], Sermanet, P.[Pierre], Zisserman, A.[Andrew],
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations,
ICCV21(9568-9577)
IEEE DOI 2203
Training, Visualization, Protocols, Transfer learning, Supervised learning, Semantics, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Cui, J.Q.[Jie-Quan], Zhong, Z.S.[Zhi-Sheng], Liu, S.[Shu], Yu, B.[Bei], Jia, J.Y.[Jia-Ya],
Parametric Contrastive Learning,
ICCV21(695-704)
IEEE DOI 2203
Adaptation models, Image recognition, Codes, Benchmark testing, Optimization, Recognition and classification, Representation learning BibRef

Islam, A.[Ashraful], Chen, C.F.[Chun-Fu], Panda, R.[Rameswar], Karlinsky, L.[Leonid], Radke, R.[Richard], Feris, R.[Rogerio],
A Broad Study on the Transferability of Visual Representations with Contrastive Learning,
ICCV21(8825-8835)
IEEE DOI 2203
Representation learning, Visualization, Adaptation models, Analytical models, Computational modeling, Transfer learning, Representation learning BibRef

Wang, J.[Jin], Jiang, B.[Bo],
Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs,
GSP-CV21(885-892)
IEEE DOI 2112
Knowledge engineering, Learning systems, Correlation, Semantics, Benchmark testing BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multiple Kernel Learning, MKL .


Last update:Aug 14, 2022 at 21:20:19