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This paper proposes a method for representation learning of multimodal data using contrastive losses.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Donglai Wei, Joseph J Lim, Andrew Zisserman, and William T Freeman · 2018
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Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph
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Fast autoaugment
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Contrastive learning with adversarial examples
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Hard negative mixing for contrastive learning
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus · 2020
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven CH Hoi · 2020
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Self-supervised multimodal versatile networks
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Are labels necessary for neural architecture search?
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P4contrast: Contrastive learning with pairs of point-pixel pairs for rgb-d scene understanding
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Contrastive learning with hard negative samples
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Efficient rgb-d semantic segmentation for indoor scene analysis
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What makes for good views for contrastive learning
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On mutual information in contrastive learning for visual representations
Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, and Noah Goodman · 2020
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Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany · 2020
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