Learning with not Enough Data Part 1: Semi-Supervised Learning
When facing a limited amount of labeled data for supervised learning tasks, four approaches are commonly discussed.
Lil'Log (Lilian Weng)25 min read
unsupervised-learning
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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.
Lil'Log (Lilian Weng)41 min readSelf-Supervised Representation Learning
[Updated on 2020-01-09: add a new section on Contrastive Predictive Coding]. [Updated on 2020-04-13: add a “Momentum Contrast” section on MoCo, SimCLR and CURL.] [Updated on 2020-07-08: add a “Bisimulation” section on DeepMDP and DBC.] [Updated on 2020-09-12: add MoCo V2 and BYOL in the “Momentum…
Lil'Log (Lilian Weng)36 min read
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