Ask Your Distribution Shift if Pre-Training is Right for You
Benjamin Cohen-Wang Joshua Vendrow Aleksander Madry
Pre-training is a widely used approach to develop models that are robust to
distribution shifts. However, in practice, its effectiveness varies:
fine-tuning a pre-trained model improves robustness significantly in some cases
but not at all in others (compared to training from scratch). In this work, we
seek to characterize the failure modes that pre-training can and cannot
address. In particular, we focus on two possible failure modes of models under
distribution shift: poor extrapolation (e.g., they cannot generalize to a
different domain) and biases in the training data (e.g., they rely on spurious
features). Our study suggests that, as a rule of thumb, pre-training can help
mitigate poor extrapolation but not dataset biases. After providing theoretical
motivation and empirical evidence for this finding, we explore two of its
implications for developing robust models: (1) pre-training and interventions
designed to prevent exploiting biases have complementary robustness benefits,
and (2) fine-tuning on a (very) small, non-diverse but de-biased dataset can
result in significantly more robust models than fine-tuning on a large and
diverse but biased dataset. Code is available at
https://github.com/MadryLab/pretraining-distribution-shift-robustness.