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Noise contrastive learning is a popular technique for unsupervised representation learning.
Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
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Contrastive estimation: Training log-linear models on unlabeled data
N. A. Smith and J. Eisner · 2005
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Natural Language Processing with Python
S. Bird, E. Klein, and E. Loper · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
M. Gutmann and A. Hyvärinen · 2010
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Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Loss is its own reward: Self-supervision for reinforcement learning
E. Shelhamer, P. Mahmoudieh, M. Argus, and T. Darrell · 2017
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Foundations of machine learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2018
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Near-optimal representation learning for hierarchical reinforcement learning
O. Nachum, S. Gu, H. Lee, and S. Levine · 2018
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Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
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Learning representations by maximizing mutual information across views
P. Bachman, R. D. Hjelm, and W. Buchwalter · 2019
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ℓ ∞ \ell_{\infty} -vector contraction for rademacher complexity
D. J. Foster and A. Rakhlin · 2019
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Contrastive learning of structured world models
T. Kipf, E. van der Pol, and M. Welling · 2019
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A theoretical analysis of contrastive unsupervised representation learning
N. Saunshi, O. Plevrakis, S. Arora, M. Khodak, and H. Khandeparkar · 2019
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On mutual information maximization for representation learning
M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic · 2019
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Debiased contrastive learning
C.-Y. Chuang, J. Robinson, Y.-C. Lin, A. Torralba, and S. Jegelka · 2020
Heterogeneous contrastive learning: Encoding spatial information for compact visual representations
X. Huo, L. Xie, L. Wei, X. Zhang, H. Li, Z. Yang, W. Zhou, H. Li, and Q. Tian · 2020
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Supervised contrastive learning
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
M. Laskin, A. Srinivas, and P. Abbeel · 2020
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Contrastive representation learning: A framework and review
P. H. Le-Khac, G. Healy, and A. F. Smeaton · 2020
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Predicting what you already know helps: Provable self-supervised learning
J. D. Lee, Q. Lei, N. Saunshi, and J. Zhuo · 2020
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Electra: Pre-training text encoders as discriminators rather than generators
K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning · 2020
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Cert: Contrastive self-supervised learning for language understanding
H. Fang and P. Xie · 2020
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A unified stochastic gradient approach to designing bayesian-optimal experiments
A. Foster, M. Jankowiak, M. O’Meara, Y. W. Teh, and T. Rainforth · 2020
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Declutr: Deep contrastive learning for unsupervised textual representations
J. M. Giorgi, O. Nitski, G. D. Bader, and B. Wang · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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C. Tosh, A. Krishnamurthy, and D. Hsu · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
T. Wang and P. Isola · 2020
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Dense contrastive learning for self-supervised visual pre-training
X. Wang, R. Zhang, C. Shen, T. Kong, and L. Li · 2020
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Masked contrastive representation learning for reinforcement learning
J. Zhu, Y. Xia, L. Wu, J. Deng, W. Zhou, T. Qin, and H. Li · 2020
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Lrc-bert: Latent-representation contrastive knowledge distillation for natural language understanding
H. Fu, S. Zhou, Q. Yang, J. Tang, G. Liu, K. Liu, and X. Li · 2021
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Contrastive learning, multi-view redundancy, and linear models
C. Tosh, A. Krishnamurthy, and D. Hsu · 2021
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