  Since out-of-distribution generalization is a generally ill-posed problem,
various proxy targets (e.g., calibration, adversarial robustness, algorithmic
corruptions, invariance across shifts) were studied across different research
programs resulting in different recommendations. While sharing the same
aspirational goal, these approaches have never been tested under the same
experimental conditions on real data. In this paper, we take a unified view of
previous work, highlighting message discrepancies that we address empirically,
and providing recommendations on how to measure the robustness of a model and
how to improve it. To this end, we collect 172 publicly available dataset pairs
for training and out-of-distribution evaluation of accuracy, calibration error,
adversarial attacks, environment invariance, and synthetic corruptions. We
fine-tune over 31k networks, from nine different architectures in the many- and
few-shot setting. Our findings confirm that in- and out-of-distribution
accuracies tend to increase jointly, but show that their relation is largely
dataset-dependent, and in general more nuanced and more complex than posited by
previous, smaller scale studies.
