On Revisiting Entropy for Identifying Mislabeled Images 事件

On Revisiting Entropy for Identifying Mislabeled Images arXiv:2605.31090v1 Announce Type: new Abstract: Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labels. We address this challenge by proposing a novel approach for mislabeled data detection that leverages training dynamics. Our method is grounded in the key observation that correctly labeled samples exhibit consistent entropy decrease during t