Lilian Weng • 12/5/2021

Learning with not Enough Data Part 1: Semi-Supervised Learning

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This article introduces a series on handling limited labeled data, focusing on Part 1: Semi-Supervised Learning. It explains the core concept of using both labeled and unlabeled data, presents the common loss function structure (L = Ls + μ(t)Lu), and contrasts its prevalence in vision tasks versus the pre-training paradigm in NLP. It defines key notations and sets the stage for detailed method discussions.

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#Machine Learning #Pre Training #Fine Tuning
Learning with not Enough Data Part 1: Semi-Supervised Learning

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