Fetching the paper…
Reading the bibliography…
Contrastive learning relies on an assumption that positive pairs contain related views, e.g., patches of an image or co-occurring multimodal signals of a video, that share certain underlying information about an instance.
G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu, “Making deep neural networks robust to label noise: A loss correction approach,” in
1952
Earlier work this paper cites.
S. Chopra, R. Hadsell, and Y. LeCun, “Learning a similarity metric discriminatively, with application to face verification,” in
2005
Earlier work this paper cites.
R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in
2006
Earlier work this paper cites.
F. H. N. H. L. Hörmander, N. S. B. Totaro, and A. V. M. Waldschmidt, “Grundlehren der mathematischen wissenschaften 332.” Springer, 2006
2006
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.”
2008
Earlier work this paper cites.
A. Krizhevsky
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in
2010
Earlier work this paper cites.
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “Hmdb: a large video database for human motion recognition,” in
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari, “Learning with noisy labels,”
2013
Earlier work this paper cites.
A. Ghosh, N. Manwani, and P. Sastry, “Making risk minimization tolerant to label noise,”
2015
Earlier work this paper cites.
S. Sukhbaatar, J. Bruna, M. Paluri, L. Bourdev, and R. Fergus, “Training convolutional networks with noisy labels,” in
2015
Earlier work this paper cites.
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang, “Learning from massive noisy labeled data for image classification,” in
2015
Earlier work this paper cites.
T. Liu and D. Tao, “Classification with noisy labels by importance reweighting,”
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in
2016
Earlier work this paper cites.
A. Ghosh, H. Kumar, and P. Sastry, “Robust loss functions under label noise for deep neural networks,” in
2017
Earlier work this paper cites.
Y. Li, J. Yang, Y. Song, L. Cao, J. Luo, and L.-J. Li, “Learning from noisy labels with distillation,” in
2017
Earlier work this paper cites.
A. Veit, N. Alldrin, G. Chechik, I. Krasin, A. Gupta, and S. Belongie, “Learning from noisy large-scale datasets with minimal supervision,” in
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Arpit, S. Jastrzebski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,”
2018
Earlier work this paper cites.
L. Logeswaran and H. Lee, “An efficient framework for learning sentence representations,” in
2018
Earlier work this paper cites.
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, S. Levine, and G. Brain, “Time-contrastive networks: Self-supervised learning from video,” in
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,”
2018
Cited alongside, same era.
L. Jiang, Z. Zhou, T. Leung, L.-J. Li, and L. Fei-Fei, “Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels,” in
2018
Cited alongside, same era.
M. Ren, W. Zeng, B. Yang, and R. Urtasun, “Learning to reweight examples for robust deep learning,” in
2018
Cited alongside, same era.
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, “Co-teaching: Robust training of deep neural networks with extremely noisy labels,”
2018
Cited alongside, same era.
Z. Zhang and M. R. Sabuncu, “Generalized cross entropy loss for training deep neural networks with noisy labels,”
2018
Cited alongside, same era.
A. Miech, J.-B. Alayrac, L. Smaira, I. Laptev, J. Sivic, and A. Zisserman, “End-to-end learning of visual representations from uncurated instructional videos,” in
2020
Later among the works it cites.
S. Ma, Z. Zeng, D. McDuff, and Y. Song, “Active contrastive learning of audio-visual video representations,” in
2020
Later among the works it cites.
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola, “What makes for good views for contrastive learning?” in
2020
Later among the works it cites.
T. Han, W. Xie, and A. Zisserman, “Self-supervised co-training for video representation learning,”
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
2018
Cited alongside, same era.
B. Korbar, D. Tran, and L. Torresani, “Cooperative learning of audio and video models from self-supervised synchronization,” in
2018
Cited alongside, same era.
D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun, and M. Paluri, “A closer look at spatiotemporal convolutions for action recognition,” in
2018
Cited alongside, same era.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in
2018
Cited alongside, same era.
B. Adhikari, Y. Zhang, N. Ramakrishnan, and B. A. Prakash, “Sub2vec: Feature learning for subgraphs,” in
2018
Cited alongside, same era.
M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz, “Invariant risk minimization,”
2019
Cited alongside, same era.
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, “Graph contrastive learning with augmentations,”
2020
Later among the works it cites.
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,”
2020
Later among the works it cites.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” in
2020
Later among the works it cites.
Y. Kalantidis, M. B. Sariyildiz, N. Pion, P. Weinzaepfel, and D. Larlus, “Hard negative mixing for contrastive learning,” in
2020
Later among the works it cites.
K. Hassani and A. H. Khasahmadi, “Contrastive multi-view representation learning on graphs,” in
2020
Later among the works it cites.
D. McAllester and K. Stratos, “Formal limitations on the measurement of mutual information,” in
2020
Later among the works it cites.
Y. M. Asano, M. Patrick, C. Rupprecht, and A. Vedaldi, “Labelling unlabelled videos from scratch with multi-modal self-supervision,” in
2020
Later among the works it cites.
H. Alwassel, D. Mahajan, B. Korbar, L. Torresani, B. Ghanem, and D. Tran, “Self-supervised learning by cross-modal audio-video clustering,” in
2020
Later among the works it cites.
2020
Later among the works it cites.
J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny, “Barlow twins: Self-supervised learning via redundancy reduction,” in
2021
Later among the works it cites.
J. Robinson, C.-Y. Chuang, S. Sra, and S. Jegelka, “Contrastive learning with hard negative samples,” in
2021
Later among the works it cites.
A. Saeed, D. Grangier, and N. Zeghidour, “Contrastive learning of general-purpose audio representations,” in
2021
Later among the works it cites.
L. Wang and A. v. d. Oord, “Multi-format contrastive learning of audio representations,”
2021
Later among the works it cites.
P. Morgado, N. Vasconcelos, and I. Misra, “Audio-visual instance discrimination with cross-modal agreement,” in
2021
Later among the works it cites.
M. Patrick, Y. M. Asano, P. Kuznetsova, R. Fong, J. F. Henriques, G. Zweig, and A. Vedaldi, “On compositions of transformations in contrastive self-supervised learning,” in
2021
Later among the works it cites.
P. Morgado, I. Misra, and N. Vasconcelos, “Robust audio-visual instance discrimination,” in
2021
Later among the works it cites.
2021
Later among the works it cites.
S. Lee, J. Chung, Y. Yu, G. Kim, T. Breuel, G. Chechik, and Y. Song, “Acav100m: Automatic curation of large-scale datasets for audio-visual video representation learning,” in
2021
Later among the works it cites.
C.-Y. Chuang, Y. Mroueh, K. Greenewald, A. Torralba, and S. Jegelka, “Measuring generalization with optimal transport,” in
2021
Later among the works it cites.
X. Chen, S. Xie, and K. He, “An empirical study of training self-supervised vision transformers,”
2021
Later among the works it cites.
X. Chen and K. He, “Exploring simple siamese representation learning,” in
2021
Later among the works it cites.
J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka, and S. Sra, “Can contrastive learning avoid shortcut solutions?” in
2021
Later among the works it cites.
Y. You, T. Chen, Y. Shen, and Z. Wang, “Graph contrastive learning automated,”
2021
Later among the works it cites.