Understanding and Improving Early Stopping for Learning with Noisy Labels

TL;DR

This paper introduces a progressive early stopping method that trains neural network layers sequentially to better handle noisy labels, leading to improved stability and state-of-the-art results in image classification.

Contribution

The paper proposes a novel progressive training approach that separates DNN layers and trains them sequentially to mitigate label noise effects.

Findings

PES outperforms traditional early stopping in noisy label scenarios.

PES achieves state-of-the-art results on image classification benchmarks.

Layer-wise training enhances robustness to label noise.

Abstract

The memorization effect of deep neural network (DNN) plays a pivotal role in many state-of-the-art label-noise learning methods. To exploit this property, the early stopping trick, which stops the optimization at the early stage of training, is usually adopted. Current methods generally decide the early stopping point by considering a DNN as a whole. However, a DNN can be considered as a composition of a series of layers, and we find that the latter layers in a DNN are much more sensitive to label noise, while their former counterparts are quite robust. Therefore, selecting a stopping point for the whole network may make different DNN layers antagonistically affected each other, thus degrading the final performance. In this paper, we propose to separate a DNN into different parts and progressively train them to address this problem. Instead of the early stopping, which trains a whole DNN all at once, we initially train former DNN layers by optimizing the DNN with a relatively large number of epochs. During training, we progressively train the latter DNN layers by using a smaller number of epochs with the preceding layers fixed to counteract the impact of noisy labels. We term the proposed method as progressive early stopping (PES). Despite its simplicity, compared with the early stopping, PES can help to obtain more promising and stable results. Furthermore, by combining PES with existing approaches on noisy label training, we achieve state-of-the-art performance on image classification benchmarks.