An Adaptive Data cleaning Framework for Noisy Label Detection 事件

An Adaptive Data cleaning Framework for Noisy Label Detection arXiv:2606.07086v1 Announce Type: new Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets. In real-world applications, however, labels are often corrupted by ambiguity, human error, or dynamic environments. Over-parameterized DNNs easily memorize these noisy labels during training, degrading model accuracy and generalization. Existing data-cleaning and sample-selection strategies often