  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.
