Late Stopping: Avoiding Confidently Learning from Mislabeled Examples
Suqin Yuan Lei Feng Tongliang Liu
Sample selection is a prevalent method in learning with noisy labels, where
small-loss data are typically considered as correctly labeled data. However,
this method may not effectively identify clean hard examples with large losses,
which are critical for achieving the model's close-to-optimal generalization
performance. In this paper, we propose a new framework, Late Stopping, which
leverages the intrinsic robust learning ability of DNNs through a prolonged
training process. Specifically, Late Stopping gradually shrinks the noisy
dataset by removing high-probability mislabeled examples while retaining the
majority of clean hard examples in the training set throughout the learning
process. We empirically observe that mislabeled and clean examples exhibit
differences in the number of epochs required for them to be consistently and
correctly classified, and thus high-probability mislabeled examples can be
removed. Experimental results on benchmark-simulated and real-world noisy
datasets demonstrate that the proposed method outperforms state-of-the-art
counterparts.