ACD-U: Asymmetric co-teaching with machine unlearning for robust learning with noisy labels

ACD-U: Asymmetric co-teaching with machine unlearning for robust learning with noisy labels explores ACD-U is a robust noisy label learning framework that leverages asymmetric co-teaching and machine unlearning to achieve state-of-the-art performance, offering a potential solution for improving model accuracy in real-world datasets with noisy labels.. Commercial viability score: 8/10 in Noisy Label Learning.