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In self-supervised visual representation learning, a feature extractor is trained on a "pretext task" for which labels can be generated cheaply, without human annotation.
Discriminative unsupervised feature learning with convolutional neural networks
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and Brox, T · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., and Philbin, J · 2015
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Youtube-8m: A large-scale video classification benchmark
Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B., and Vijayanarasimhan, S · 2016
Earlier work this paper cites.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
Earlier work this paper cites.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Earlier work this paper cites.
Colorful image colorization
Zhang, R., Isola, P., and Efros, A. A · 2016
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2016
Earlier work this paper cites.
Multi-task self-supervised visual learning
Doersch, C. and Zisserman, A · 2017
Earlier work this paper cites.
Unsupervised representation learning by sorting sequences
Lee, H.-Y., Huang, J.-B., Singh, M., and Yang, M.-H · 2017
Cited alongside, same era.
Representation learning by learning to count
Noroozi, M., Pirsiavash, H., and Favaro, P · 2017
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Akhtar, N. and Mian, A · 2018
Cited alongside, same era.
Deep clustering for unsupervised learning of visual features
Caron, M., Bojanowski, P., Joulin, A., and Douze, M · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
Cited alongside, same era.
Self-Supervised feature learning by learning to spot artifacts
Jenni, S. and Favaro, P · 2018
Cited alongside, same era.
Self-supervised representation learning by rotation feature decoupling
Feng, Z., Xu, C., and Tao, D · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
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Scaling and benchmarking self-supervised visual representation learning
Goyal, P., Mahajan, D., Gupta, A., and Misra, I · 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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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y · 2019
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Koyama, M., and Ishii, S · 2018
Cited alongside, same era.
Improvements to context based self-supervised learning
Mundhenk, T. N., Ho, D., and Chen, B. Y · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
Tempered adversarial networks
Sajjadi, M. S., Parascandolo, G., Mehrjou, A., and Schölkopf, B · 2018
Cited alongside, same era.
Learning and using the arrow of time
Wei, D., Lim, J. J., Zisserman, A., and Freeman, W. T · 2018
Cited alongside, same era.
Revisiting self-supervised visual representation learning
Kolesnikov, A., Zhai, X., and Beyer, L · 2019
Later among the works it cites.
Self-supervised learning of pretext-invariant representations
Misra, I. and van der Maaten, L · 2019
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Image synthesis with a single (robust) classifier
Santurkar, S., Ilyas, A., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
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Tian, Y., Krishnan, D., and Isola, P · 2019
Later among the works it cites.
A large-scale study of representation learning with the visual task adaptation benchmark
Zhai, X., Puigcerver, J., Kolesnikov, A., Ruyssen, P., Riquelme, C., Lucic, M., Djolonga, J., Pinto, A. S., Neumann, M., Dosovitskiy, A., Beyer, L., Bachem, O., Tschannen, M., Michalski, M., Bousquet, O., Gelly, S., and Houlsby, N · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Closest in time.
Adversarial examples improve image recognition
Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A., and Le, Q. V · 2020
Closest in time.