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Self-supervised learning aims to learn a embedding space where semantically similar samples are close.
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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An analysis of single-layer networks in unsupervised feature learning
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Distributed representations of words and phrases and their compositionality
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Generative adversarial nets
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Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman · 2015
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Adam: A method for stochastic optimization
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Faster R-CNN: towards real-time object detection with region proposal networks
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Deep residual learning for image recognition
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Generalized Principal Component Analysis , volume 40 of Interdisciplinary applied mathematics
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Whitening for self-supervised representation learning
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Provable guarantees for self-supervised deep learning with spectral contrastive loss
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Prototypical contrastive learning of unsupervised representations
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A note on connecting barlow twins with negative-sample-free contrastive learning
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Barlow twins: Self-supervised learning via redundancy reduction
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What makes instance discrimination good for transfer learning?
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Your contrastive learning is secretly doing stochastic neighbor embedding
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Chaos is a ladder: A new theoretical understanding of contrastive learning via augmentation overlap
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