Title: Class Prototype-based Cleaner for Label Noise Learning. Abstract: Semi-supervised learning based methods are current SOTA solutions to the noisy-label learning problem, which rely on learning an unsupervised label cleaner first to divide the training samples into a   clean labeled set  and a   noisy unlabeled set. Typically, the cleaner is   obtained  via fitting a mixture model to  the distribution of  per-sample training losses.  However, the modeling procedure is  \emph{class agnostic} and  assumes the loss distributions of clean and noisy samples are the same across different classes. Unfortunately, in practice, such an assumption does not always hold due to the varying learning difficulty of different classes, thus leading to sub-optimal   label noise partition criteria. In this work, we first reveal this long-ignored problem and propose a simple yet effective solution, named  \textbf{C}lass \textbf{P}rototype-based label noise \textbf{C}leaner (\textbf{CPC}). Unlike previous works treating all the classes equally, CPC fully considers loss distribution heterogeneity and applies  class-aware modulation to partition the clean and noisy data. CPC takes advantage of loss distribution modeling and intra-class consistency regularization in feature space simultaneously  and thus can  better distinguish clean and noisy labels. We theoretically justify the effectiveness of our method by explaining it from the Expectation-Maximization (EM) framework. Extensive experiments are conducted on the noisy-label benchmarks CIFAR-10, CIFAR-100, Clothing1M and WebVision. The results show that CPC brings about impressive performance improvement across all benchmarks. Moreover, CPC shows outstanding performance especially in the extremely noisy scenarios, and improves the   accuracy on CIFAR-100 at 90\% noise rate   by as high as 13\% over the SOTAs.