The Role of Pre-training Data in Transfer Learning
This work addresses the problem of optimizing pre-training data selection for transfer learning in machine learning, providing insights for researchers and practitioners, though it is incremental as it builds on existing paradigms.
The study investigated how pre-training data distribution affects transfer learning performance, finding that data source choice is crucial for few-shot transfer but less so with more fine-tuning data, and that using 2000X more data from LAION can match supervised ImageNet pre-training performance.
The transfer learning paradigm of model pre-training and subsequent fine-tuning produces high-accuracy models. While most studies recommend scaling the pre-training size to benefit most from transfer learning, a question remains: what data and method should be used for pre-training? We investigate the impact of pre-training data distribution on the few-shot and full fine-tuning performance using 3 pre-training methods (supervised, contrastive language-image and image-image), 7 pre-training datasets, and 9 downstream datasets. Through extensive controlled experiments, we find that the choice of the pre-training data source is essential for the few-shot transfer, but its role decreases as more data is made available for fine-tuning. Additionally, we explore the role of data curation and examine the trade-offs between label noise and the size of the pre-training dataset. We find that using 2000X more pre-training data from LAION can match the performance of supervised ImageNet pre-training. Furthermore, we investigate the effect of pre-training methods, comparing language-image contrastive vs. image-image contrastive, and find that the latter leads to better downstream accuracy