The Role of Pre-training Data in Transfer Learning

Rahim Entezari, Mitchell Wortsman, Olga Saukh, M. Moein Shariatnia, Hanie Sedghi, Ludwig Schmidt

Keywords: pretraining, transfer learning, supervised training, constrastive learning, clip, simclr

TL;DR: We investigate the role of pretraining distribution, data curation, size, and loss and downstream transfer learning

Abstract: The transfer learning paradigm of model pre-training and subsequent fine-tuning produces high accuracy models. However, a question remains: what data and method should be used for pre-training? We study the effect of the pre-training distribution on transfer learning in the context of image classification. Through controlled experiments, we find that the pre-training dataset is initially important for low-shot transfer. However, the differences between distributions is diminished as more data is made available for fine-tuning. Still, fine-tuning outperforms training from scratch. We also investigate dataset size and observe that larger pre-training datasets lead to better accuracy, however, the absolute accuracy difference is largest in the few-shot regime. Beyond data, we study the effect of the pre-training method, language-image contrastive vs. image-image contrastive, finding that the latter usually leads to better transfer accuracy

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