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Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables.
Matrix multiplication via arithmetic progressions
Don Coppersmith and Shmuel Winograd · 1987
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Gradient-based learning applied to document recognition
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Stable signal recovery from incomplete and inaccurate measurements
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Kernel measures of conditional dependence
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Learning multiple layers of features from tiny images
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The caltech-ucsd birds-200-2011 dataset
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Kernel embeddings of conditional distributions: A unified kernel framework for nonparametric inference in graphical models
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Generative adversarial nets
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Fine-grained visual comparisons with local learning
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Imagenet large scale visual recognition challenge
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Deep residual learning for image recognition
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Domain generalization by marginal transfer learning
Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott · 2017
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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A simple framework for contrastive learning of visual representations
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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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Hard negative mixing for contrastive learning
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Predicting what you already know helps: Provable self-supervised learning
Jason D Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo · 2020
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C-mi-gan: Estimation of conditional mutual information using minmax formulation
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Swapping autoencoder for deep image manipulation
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Contrastive learning with hard negative samples
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Conditional negative sampling for contrastive learning of visual representations
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Learning transferable visual models from natural language supervision
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D2c: Diffusion-denoising models for few-shot conditional generation
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Noise contrastive meta-learning for conditional density estimation using kernel mean embeddings
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