Measuring Robustness to Natural Distribution Shifts in Image Classification.

We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbations (noise, simulated weather artifacts, adversarial examples, etc.), which leaves open how robustness on synthetic distribution shift relates to distribution shift arising in real data. Informed by an evaluation of 196 ImageNet models in 211 different test conditions, we find that there is little to no transfer of robustness from current synthetic to natural distribution shift. Moreover, most current techniques provide no robustness to the natural distribution shifts in our testbed. The main exception is training on larger datasets, which in some cases offers small gains in robustness. Our results indicate that distribution shifts arising in real data are currently an open research problem.