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On the Generalization Ability of Unsupervised Pretraining
March 12, 2024, 4:42 a.m. | Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi
cs.LG updates on arXiv.org arxiv.org
Abstract: Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization. However, a rigorous understanding of how the representation function learned on an unlabeled dataset affects the generalization of the fine-tuned model is lacking. Existing theoretical research does not adequately account for the heterogeneity of the distribution and tasks in pre-training and fine-tuning stage. To bridge this gap, this paper introduces a novel theoretical framework that illuminates the critical …
abstract advances arxiv cs.lg dataset fine-tuning function however model generalization pre-training pretraining representation research stat.ml training type understanding unsupervised unsupervised learning
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