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Instance discriminative self-supervised representation learning has been attracted attention thanks to its unsupervised nature and informative feature representation for downstream tasks.
Self-Organization in a Perceptual Network
R. Linsker · 1988
Earlier work this paper cites.
An Unsupervised Learning Procedure that Discovers Surfaces in Random-dot Stereograms
G. E. Hinton and S. Becker · 1990
Earlier work this paper cites.
Birthday Paradox, Coupon Collectors, Caching Algorithms and Self-organizing Search
P. Flajolet, D. Gardy, and L. Thimonier · 1992
Earlier work this paper cites.
A Coupon Collector’s Problem with Bonuses
T. Nakata and I. Kubo · 2006
Earlier work this paper cites.
Matplotlib: A 2D graphics environment
J. D. Hunter · 2007
Earlier work this paper cites.
Extracting and Composing Robust Features with Denoising Autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
A. Krizhevsky · 2009
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and Édouard Duchesnay · 2011
Earlier work this paper cites.
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
M. U. Gutmann and A. Hyvärinen · 2012
Earlier work this paper cites.
Metric Learning: A Survey
B. Kulis · 2012
Earlier work this paper cites.
Representation Learning: A Review and New Perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
Earlier work this paper cites.
Distributed Representations of Words and Phrases and their Compositionality
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean · 2013
Earlier work this paper cites.
On the Importance of Initialization and Momentum in Deep Learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
Earlier work this paper cites.
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Skip-Thought Vectors
R. Kiros, Y. Zhu, R. Salakhutdinov, R. S. Zemel, A. Torralba, R. Urtasun, and S. Fidler · 2015
Earlier work this paper cites.
That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets
W. Y. Wang and D. Yang · 2015
Earlier work this paper cites.
Character-level Convolutional Networks for Text Classification
X. Zhang, J. Zhao, and Y. LeCun · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Learning Representations for Automatic Colorization
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
Earlier work this paper cites.
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
M. Noroozi and P. Favaro · 2016
Earlier work this paper cites.
Context Encoders: Feature Learning by Inpainting
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. A. Efros · 2016
Earlier work this paper cites.
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
M. K. Titsias · 2016
Earlier work this paper cites.
Colorful Image Colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
Earlier work this paper cites.
Bag of Tricks for Efficient Text Classification
A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov · 2017
Earlier work this paper cites.
SGDR: Stochastic Gradient Descent with Warm Restarts
I. Loshchilov and F. Hutter · 2017
Cited alongside, same era.
Prototypical Networks for Few-shot Learning
J. Snell, K. S. Twitter, and R. S. Zemel · 2017
Cited alongside, same era.
Large Batch Training of Convolutional Networks
Y. You, I. Gitman, and B. Ginsburg · 2017
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Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
Cited alongside, same era.
Classification from Pairwise Similarity and Unlabeled Data
H. Bao, G. Niu, and M. Sugiyama · 2018
Cited alongside, same era.
Deep Clustering for Unsupervised Learning of Visual Features
M. Caron, P. Bojanowski, A. Joulin, and M. Douze · 2018
Momentum Contrast for Unsupervised Visual Representation Learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Data-Efficient Image Recognition with Contrastive Predictive Coding
O. J. Hénaff, A. Srinivas, J. De Fauw, A. Razavi, C. Doersch, S. M. A. Eslami, and A. Van den oord · 2020
Later among the works it cites.
Hard Negative Mixing for Contrastive Learning
Y. Kalantidis, M. B. Sariyildiz, N. Pion, P. Weinzaepfel, and D. Larlus · 2020
Later among the works it cites.
Contrastive Representation Learning: A Framework and Review
P. H. Le-Khac, G. Healy, and A. F. Smeaton · 2020
Later among the works it cites.
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
S. Li, Y. Zhao, R. Varma, O. Salpekar, P. Noordhuis, T. Li, A. Paszke, J. Smith, B. Vaughan, P. Damania, and S. Chintala · 2020
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Formal Limitations on the Measurement of Mutual Information
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Unsupervised Representation Learning by Predicting Image Rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
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An Efficient Framework for Learning Sentence Representations
L. Logeswaran and H. Lee · 2018
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Advances in Pre-Training Distributed Word Representations
T. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch, and A. Joulin · 2018
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Representation Learning with Contrastive Predictive Coding
A. van den Oord, Y. Li, and O. Vinyals · 2018
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Unsupervised Feature Learning via Non-Parametric Instance Discrimination
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin · 2018
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A Theoretical Analysis of Contrastive Unsupervised Representation Learning
S. Arora, H. Khandeparkar, M. Khodak, O. Plevrakis, and N. Saunshi · 2019
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D. McAllester and K. Stratos · 2020
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How Useful is Self-Supervised Pretraining for Visual Tasks?
A. Newell and J. Deng · 2020
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PAC-Bayesian Contrastive Unsupervised Representation Learning
K. Nozawa, P. Germain, and B. Guedj · 2020
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pandas-dev/pandas: Pandas 1.0.3, Mar. 2020
J. Reback, W. McKinney, jbrockmendel, J. V. den Bossche, T. Augspurger, P. Cloud, gfyoung, Sinhrks, A. Klein, M. Roeschke, S. Hawkins, J. Tratner, C. She, W. Ayd, T. Petersen, M. Garcia, J. Schendel, A. Hayden, MomIsBestFriend, V. Jancauskas, P. Battiston, S. Seabold, chris-b1, h-vetinari, S. Hoyer, W. Overmeire, alimcmaster1, K. Dong, C. Whelan, and M. Mehyar · 2020
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GNU Parallel, Nov. 2020
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On Mutual Information Maximization for Representation Learning
M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, İ. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors · 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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Investigating the Role of Negatives in Contrastive Representation Learning
J. T. Ash, S. Goel, A. Krishnamurthy, and D. Misra · 2021
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For Self-Supervised Learning, Rationality Implies Generalization, Provably
Y. Bansal, G. Kaplun, and B. Barak · 2021
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Exploring Simple Siamese Representation Learning
X. Chen and K. He · 2021
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SimCSE: Simple Contrastive Learning of Sentence Embeddings
T. Gao, X. Yao, and D. Chen · 2021
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Predicting What You Already Know Helps: Provable Self-Supervised Learning
J. D. Lee, Q. Lei, N. Saunshi, and J. Zhuo · 2021
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Representation Learning via Invariant Causal Mechanisms
J. Mitrovic, B. McWilliams, J. Walker, L. Buesing, and C. Blundell · 2021
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A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks
N. Saunshi, S. Malladi, and S. Arora · 2021
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A Survey on Semi-, Self-and Unsupervised Learning in Image Classification
L. Schmarje, M. Santarossa, S.-M. Schröder, and R. Koch · 2021
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Understanding Self-Supervised Learning Dynamics without Contrastive Pairs
Y. Tian, X. Chen, and S. Ganguli · 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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