label noise in real-world datasets encodes wrong correlation patterns and
impairs the generalization of deep neural networks (DNNs). It is critical to
find efficient ways to detect corrupted patterns. Current met
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.