The memorization effect of deep neural networks (DNNs) plays a pivotal role
in recent label noise learning methods. To exploit this effect, the model
prediction-based methods have been widely adopted, which aim to exploit the
outputs of DNNs in the early stage of learning to correct noisy labels.
However, we observe that the model will make mistakes during label prediction,
resulting in unsatisfactory performance. By contrast, the produced features in
the early stage of learning show better robustness. Inspired by this
observation, in this paper, we propose a novel feature embedding-based method
for deep learning with label noise, termed LabEl NoiseDilution (LEND). To be
specific, we first compute a similarity matrix based on current embedded
features to capture the local structure of training data. Then, the noisy
supervision signals carried by mislabeled data are overwhelmed by nearby
correctly labeled ones (\textit{i.e.}, label noise dilution), of which the
effectiveness is guaranteed by the inherent robustness of feature embedding.
Finally, the training data with diluted labels are further used to train a
robust classifier. Empirically, we conduct extensive experiments on both
synthetic and real-world noisy datasets by comparing our LEND with several
representative robust learning approaches. The results verify the effectiveness
of our LEND.

本文提出了一种基于特征嵌入的深度学习带标签噪声的方法（LEND），该方法能利用嵌入特征的内在韧性来有效减少误标签，采用深度学习进行训练并获得鲁棒性分类器。

利用特征嵌入实现对带有噪声标签的坚韧学习

Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels

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.

本论文提出一种分步训练深度神经网络的方法，称为渐进式提前停止（Progressive Early Stopping, PES），以对抗训练过程中的标签噪声。该方法可以显著提高图像分类质量，并结合现有标签噪声训练方法，达到了最新成果。