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arXiv · 2207.05739

A Data-Based Perspective on Transfer Learning

Saachi Jain·Hadi Salman·Alaa Khaddaj·Eric Wong·Sung Min Park·Aleksander Madry
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Abstract

It is commonly believed that in transfer learning including more pre-training data translates into better performance. However, recent evidence suggests that removing data from the source dataset can actually help too. In this work, we take a closer look at the role of the source dataset's composition in transfer learning and present a framework for probing its impact on downstream performance. Our framework gives rise to new capabilities such as pinpointing transfer learning brittleness as well as detecting pathologies such as data-leakage and the presence of misleading examples in the source dataset. In particular, we demonstrate that removing detrimental datapoints identified by our framework improves transfer learning performance from ImageNet on a variety of target tasks. Code is available at https://github.com/MadryLab/data-transfer

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Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Madry. 2022-07-12. A Data-Based Perspective on Transfer Learning. https://arxiv.org/abs/2207.05739

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