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New CILN framework generates explicit instance-dependent label noise benchmarks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called CILN for generating synthetic instance-dependent label noise (IDN) benchmarks. Unlike previous methods that implicitly generated noise, CILN uses controlled input corruptions and a diverse voter pool to create benchmarks where the source and severity of ambiguity are explicit. This approach, tested on CIFAR10, MNIST, and Adult datasets, generates benchmarks that exhibit genuine instance-dependent noise and can reveal failure modes in existing noisy-label learning methods like Co-Teaching and DivideMix. AI

RANK_REASON The cluster contains an academic paper detailing a new research framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New CILN framework generates explicit instance-dependent label noise benchmarks

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The cluster contains an academic paper detailing a new research framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shadman Islam, Agustinus Kristiadi, Mostafa Milani ·

    Benchmarking Instance-Dependent Label Noise with Controlled Corruptions

    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 sour…

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