Robustness May be More Brittle than We Think under Different Degrees of Distribution Shifts

TL;DR

This paper investigates how model robustness varies across different degrees of distribution shifts, revealing that models, including large pre-trained ones like CLIP, can be more brittle than previously thought under diverse OOD conditions.

Contribution

The study introduces a nuanced evaluation framework across various shift degrees, highlighting the fragility of models and the limited generalization of pre-trained models under certain distribution shifts.

Findings

Model robustness varies significantly with shift degree.

Large pre-trained models are sensitive to small distribution shifts.

Evaluations should consider a broad range of shift degrees.

Abstract

Out-of-distribution (OOD) generalization is a complicated problem due to the idiosyncrasies of possible distribution shifts between training and test domains. Most benchmarks employ diverse datasets to address this issue; however, the degree of the distribution shift between the training domains and the test domains of each dataset remains largely fixed. This may lead to biased conclusions that either underestimate or overestimate the actual OOD performance of a model. Our study delves into a more nuanced evaluation setting that covers a broad range of shift degrees. We show that the robustness of models can be quite brittle and inconsistent under different degrees of distribution shifts, and therefore one should be more cautious when drawing conclusions from evaluations under a limited range of degrees. In addition, we observe that large-scale pre-trained models, such as CLIP, are sensitive to even minute distribution shifts of novel downstream tasks. This indicates that while pre-trained representations may help improve downstream in-distribution performance, they could have minimal or even adverse effects on generalization in certain OOD scenarios of the downstream task if not used properly. In light of these findings, we encourage future research to conduct evaluations across a broader range of shift degrees whenever possible.