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Self-Prediction and Contrastive Learning

Self-Supervised LearningSelf-Prediction and Contrastive LearningLilian Weng, Jong Wook KimNeurIPS 2021 Tutorial Introduction: motivation, basic concepts, examples. Early work: look into connection with old methods. Methods Self-Prediction Contrastive Learning Pretext tasks: a wide range of literature review. Techniques: improve training efficiency. Future directionsOutline2 IntroductionWhat is self-supervised Learning and why we need it?3 Self-Supervised Learning (SSL) is a special type of representation Learning that enables Learning good data representation from unlabelled is motivated by the idea of constructing supervised Learning tasks out of unsupervised is Self-Supervised Learning ?

Contrastive Learning: Inter-Sample Classification Given both similar (“positive”) and dissimilar (“negative”) candidates, to identify which ones are similar to the anchor data point is a classification task. There are creative ways to construct a set of data point candidates: 1. The original input and its distorted version

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