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This paper presents SimCLR: a simple framework for contrastive learning of visual representations.
Self-organizing neural network that discovers surfaces in random-dot stereograms
Becker, S. and Hinton, G. E · 1992
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2004
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Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., and LeCun, Y · 2006
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A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y.-W · 2006
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Visualizing data using t-sne
Maaten, L. v. d. and Hinton, G · 2008
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C · 2012
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Some improvements on deep convolutional neural network based image classification
Howard, A. G · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Collecting a large-scale dataset of fine-grained cars
Krause, J., Deng, J., Stark, M., and Fei-Fei, L · 2013
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Fine-grained visual classification of aircraft
Maji, S., Kannala, J., Rahtu, E., Blaschko, M., and Vedaldi, A · 2013
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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Birdsnap: Large-scale fine-grained visual categorization of birds
Berg, T., Liu, J., Lee, S. W., Alexander, M. L., Jacobs, D. W., and Belhumeur, P. N · 2014
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Food-101–mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Van Gool, L · 2014
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and Brox, T · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Cited alongside, same era.
Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Wu, Z., Xiong, Y., Yu, S. X., and Lin, D · 2018
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A critical analysis of self-supervision, or what we can learn from a single image
Asano, Y. M., Rupprecht, C., and Vedaldi, A · 2019
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Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 2019
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Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C. A · 2019
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Self-supervised gans via auxiliary rotation loss
Chen, T., Zhai, X., Ritter, M., Lucic, M., and Houlsby, N · 2019
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Autoaugment: Learning augmentation strategies from data
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Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., and Philbin, J · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
Sohn, K · 2016
Cited alongside, same era.
Colorful image colorization
Zhang, R., Isola, P., and Efros, A. A · 2016
Cited alongside, same era.
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2019
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Large scale adversarial representation learning
Donahue, J. and Simonyan, K · 2019
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2019
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Data-efficient image recognition with contrastive predictive coding
Hénaff, O. J., Razavi, A., Doersch, C., Eslami, S., and Oord, A. v. d · 2019
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Invariant information clustering for unsupervised image classification and segmentation
Ji, X., Henriques, J. F., and Vedaldi, A · 2019
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Revisiting self-supervised visual representation learning
Kolesnikov, A., Zhai, X., and Beyer, L · 2019
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Do better ImageNet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V · 2019
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Self-supervised learning of pretext-invariant representations
Misra, I. and van der Maaten, L · 2019
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Tian, Y., Krishnan, D., and Isola, P · 2019
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On mutual information maximization for representation learning
Tschannen, M., Djolonga, J., Rubenstein, P. K., Gelly, S., and Lucic, M · 2019
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Unsupervised data augmentation
Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., and Le, Q. V · 2019
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Unsupervised embedding learning via invariant and spreading instance feature
Ye, M., Zhang, X., Yuen, P. C., and Chang, S.-F · 2019
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S4l: Self-supervised semi-supervised learning
Zhai, X., Oliver, A., Kolesnikov, A., and Beyer, L · 2019
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Local aggregation for unsupervised learning of visual embeddings
Zhuang, C., Zhai, A. L., and Yamins, D · 2019
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., and Raffel, C · 2020
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