Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift

Knowing if a model will generalize to data `in the wild' is crucial for safe deployment. To this end, we study model disagreement notions that consider the full predictive distribution - specifically disagreement based on Hellinger distance and Kullback–Leibler divergence. We find that divergence-based scores provide better test error estimates and detection rates on out-of-distribution data compared to their top 1 counterparts. Experiments involve standard vision and foundation models.