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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

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arXiv:2606. 11699v1 Announce Type: new Abstract: The performance of machine learning and deep learning models largely depends on the quality of the training data.

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benchmarks

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benchmarks

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