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Contrastive learning (CL) methods effectively learn data representations in a self-supervision manner, where the encoder contrasts each positive sample over multiple negative samples via a one-vs-many softmax cross-entropy loss.
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Big self-supervised models are strong semi-supervised learners
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The pascal visual object classes (VOC) challenge
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Self-supervised pre-training with hard examples improves visual representations
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Mask R-CNN
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Normface: L2 hypersphere embedding for face verification
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Adversarial contrastive estimation
Avishek Joey Bose, Huan Ling, and Yanshuai Cao · 2018
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Hyperspherical variational auto-encoders
Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak · 2018
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Learning deep representations by mutual information estimation and maximization
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Cooperative learning of audio and video models from self-supervised synchronization
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An efficient framework for learning sentence representations
Lajanugen Logeswaran and Honglak Lee · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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On variational lower bounds of mutual information
Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A Alemi, and George Tucker · 2018
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Unsupervised feature learning via non-parametric instance discrimination
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Learning representations by maximizing mutual information across views
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Data-efficient image recognition with contrastive predictive coding
Imbalanced image classification with complement cross entropy
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Self-supervised learning of pretext-invariant representations
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CURL: Contrastive unsupervised representations for reinforcement learning
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Locality and compositionality in zero-shot learning
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Unsupervised representation learning by invariance propagation
Feng Wang, Huaping Liu, Di Guo, and Sun Fuchun · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
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A theoretical analysis of contrastive unsupervised representation learning
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On mutual information maximization for representation learning
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Deep Graph Infomax
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