arXiv:2506.16950v2 Announce Type: replace-cross Abstract: 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. Consequently, these benchmarks are no longer well-suited for evaluating OOD ro
The rapid advancement of web-scale vision models necessitates new evaluation benchmarks that accurately reflect the challenges of real-world out-of-distribution scenarios.
Improved OOD benchmarks are crucial for building more robust and reliable AI systems, directly impacting their deployment across various critical applications and their trustworthiness.
The existing benchmarks like ImageNet-C are becoming obsolete for evaluating the current generation of large vision models due to their exposure to common corruptions in training data, requiring new, more challenging datasets like LAION-C.
- · AI researchers improving model robustness
- · Developers of safety-critical AI applications
- · Organizations focused on AI trustworthiness
- · Developers relying solely on outdated benchmarks
- · AI models with poor OOD generalization
- · Computer vision benchmarking methodologies that remain static
The release of LAION-C will lead to a new wave of research focused on improving OOD robustness in large vision models.
AI models will become more reliable and performant in diverse, real-world conditions, reducing unexpected failures.
Increased robustness will accelerate the adoption of AI in sensitive applications like autonomous driving and medical diagnostics, reshaping industry standards.
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Read at arXiv cs.LG