AI RESEARCH
LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models
arXiv CS.LG
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ArXi:2506.16950v2 Announce Type: replace-cross Out-of-distribution (OOD) robustness is a desired property of computer vision models. Improving model robustness requires high-quality signals from robustness benchmarks to quantify progress. While various benchmark datasets such as ImageNet-C were proposed in the ImageNet era, most ImageNet-C corruption types are no longer OOD relative to today's large, web-scraped datasets, which already contain common corruptions such as blur or JPEG compression artifacts.