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Contrastive Representation Learning

The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning.
Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and one negative sample are involved. ....

Prannay Khosla , Wang Isola , Salakhutdinov Hinton , Jason Wei , Ekind Cubuk , Lajanugen Logeswaran , Phillip Isola , Dmitry Kalenichenko , Tianyu Gao , Yazhe Li Oriol Vinyals , Joshua Robinson , Logeswaran Lee , Ching Yao Chuang , Monte Carlo , Geoffrey Hinton , Mathilde Caron , Florian Schroff , Iryna Gurevych , Geoff Hinton , Cutmix Yun , Nicolas Papernot , Aapo Hyv , Bohan Li , Dinghan Shen , Sumit Chopra , Wei Zou ,

Contrastive Representation Learning

Contrastive Representation Learning
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Prannay Khosla , Wang Isola , Salakhutdinov Hinton , Jason Wei , Ekind Cubuk , Lajanugen Logeswaran , Phillip Isola , Dmitry Kalenichenko , Tianyu Gao , Yazhe Li Oriol Vinyals , Joshua Robinson , Logeswaran Lee , Ching Yao Chuang , Monte Carlo , Geoffrey Hinton , Mathilde Caron , Florian Schroff , Iryna Gurevych , Geoff Hinton , Cutmix Yun , Nicolas Papernot , Aapo Hyv , Deepcluster Caron , Bohan Li , Dinghan Shen , Sumit Chopra ,