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News/Noise-Aware Framework for Correcting Corrupted Labels
arxiv
PublishedJune 11, 2026 at 4:00 AM

Noise-Aware Framework for Correcting Corrupted Labels

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arXiv:2606.11695v1 Announce Type: cross Abstract: High-quality labeled data is essential for training reliable ML/DL models. However, real-world datasets often contain a considerable proportion of corrupted labels, which can severely degrade model performance. To address this problem, we propose CAN

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