A Meta-Analysis of Distributionally-Robust Models. (arXiv:2206.07565v1 [cs.CV])

Web: http://arxiv.org/abs/2206.07565

State-of-the-art image classifiers trained on massive datasets (such as
ImageNet) have been shown to be vulnerable to a range of both intentional and
incidental distribution shifts. On the other hand, several recent classifiers
with favorable out-of-distribution (OOD) robustness properties have emerged,
achieving high accuracy on their target tasks while maintaining their
in-distribution accuracy on challenging benchmarks. We present a meta-analysis
on a wide range of publicly released models, most of which have been published
over the last twelve months. Through …

analysis arxiv cv meta meta-analysis models