google-deepmind/distribution_shift_framework
This repository contains the code of the distribution shift framework presented in A Fine-Grained Analysis on Distribution Shift (Wiles et al., 2022).
This framework helps machine learning researchers understand how different training methods perform when the data distribution shifts between training and testing. It takes your specified dataset, model architecture, and training algorithm, and allows you to simulate various real-world data shift scenarios, outputting performance metrics for each. It's designed for researchers evaluating the robustness of machine learning models to changes in data.
Use this if you are a machine learning researcher focused on evaluating and improving model generalization under various forms of data distribution shift, such as unseen data or spurious correlations.
Not ideal if you are looking for a plug-and-play solution to deploy models, or if you are not actively researching machine learning generalization and distribution shifts.
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Python
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Apache-2.0
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Last pushed
Feb 20, 2026
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