arXiv Machine Learning June 16, 2026 By Shadman Islam, Agustinus Kristiadi, Mostafa Milani

Benchmarking Instance-Dependent Label Noise with Controlled Corruptions

Read the original on arXiv Machine Learning →

arXiv:2606. 14965v1 Announce Type: new Abstract: Synthetic instance-dependent label noise (IDN) benchmarks are widely used to evaluate noisy-label learning methods, yet existing approaches typically generate noise through imperfect annotators or classifier raters, leaving the source of ambiguity implicit.

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