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Self-supervised representation learning is heavily dependent on data augmentations to specify the invariances encoded in representations.
Introduction to computer graphics , volume 55
James D Foley, Andries Van Dam, Steven K Feiner, John F Hughes, and Richard L Phillips · 1994
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Random fields and geometry , volume 80
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Deep clustering for unsupervised learning of visual features
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Representation learning with contrastive predictive coding
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Unsupervised learning of visual features by contrasting cluster assignments
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Exploring simple siamese representation learning
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Why do self-supervised models transfer? investigating the impact of invariance on downstream tasks
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Masked autoencoders are scalable vision learners
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Towards democratizing joint-embedding self-supervised learning
Florian Bordes, Randall Balestriero, and Pascal Vincent · 2023
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Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton
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