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 t
This paper proposes a method to improve the robustness of deep learning models in the presence of noisy labels by utilizing unsupervised learning and cluster regularization.