  Deep learning models are challenged by the distribution shift between the
training data and test data. Recently, the large models pre-trained on diverse
data have demonstrated unprecedented robustness to various distribution shifts.
However, fine-tuning these models can lead to a trade-off between
in-distribution (ID) performance and out-of-distribution (OOD) robustness.
Existing methods for tackling this trade-off do not explicitly address the OOD
robustness problem. In this paper, based on causal analysis of the
aforementioned problems, we propose a novel fine-tuning method, which uses
masked images as counterfactual samples that help improve the robustness of the
fine-tuning model. Specifically, we mask either the semantics-related or
semantics-unrelated patches of the images based on class activation map to
break the spurious correlation, and refill the masked patches with patches from
other images. The resulting counterfactual samples are used in feature-based
distillation with the pre-trained model. Extensive experiments verify that
regularizing the fine-tuning with the proposed masked images can achieve a
better trade-off between ID and OOD performance, surpassing previous methods on
the OOD performance. Our code is available at
https://github.com/Coxy7/robust-finetuning.
