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Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge.
Revisiting self-supervised visual representation learning
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Faster r-cnn: Towards real-time object detection with region proposal networks
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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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Leveraging large-scale uncurated data for unsupervised pre-training of visual features
Caron, M., Bojanowski, P., Mairal, J., and Joulin, A · 2019
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Large scale adversarial representation learning
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Momentum contrast for unsupervised visual representation learning
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Perceptual straightening of natural videos
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Self-supervised learning of pretext-invariant representations
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Unsupervised Data Augmentation
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S 4 L {S}^{4}{L} : Self-supervised semi-supervised learning
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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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