AIBullisharXiv – CS AI · 7h ago6/10
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Noise-Aware Framework for Correcting Corrupted Labels
Researchers introduce CANOLA, a framework that corrects corrupted labels in datasets by estimating noise distributions and iteratively refining labels through noise-aware deep learning. The approach achieves 19-52% error reduction compared to existing methods and enables simpler models trained on corrected data to outperform complex alternatives by up to 67%.