New CILN framework generates explicit instance-dependent label noise benchmarks

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 →

• Adult
• CIFAR10
• Co-Teaching
• DivideMix
• instance-dependent label noise
• MNIST database

• paper
• other

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