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A Survey on Contrastive Self-Supervised Learning
2020
Technologies
Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample
doi:10.3390/technologies9010002
fatcat:j7lkmrb2prd5vbjdof5p3mf2ke