  Deep models trained with noisy labels are prone to over-fitting and struggle
in generalization. Most existing solutions are based on an ideal assumption
that the label noise is class-conditional, i.e., instances of the same class
share the same noise model, and are independent of features. While in practice,
the real-world noise patterns are usually more fine-grained as
instance-dependent ones, which poses a big challenge, especially in the
presence of inter-class imbalance. In this paper, we propose a two-stage clean
samples identification method to address the aforementioned challenge. First,
we employ a class-level feature clustering procedure for the early
identification of clean samples that are near the class-wise prediction
centers. Notably, we address the class imbalance problem by aggregating rare
classes according to their prediction entropy. Second, for the remaining clean
samples that are close to the ground truth class boundary (usually mixed with
the samples with instance-dependent noises), we propose a novel
consistency-based classification method that identifies them using the
consistency of two classifier heads: the higher the consistency, the larger the
probability that a sample is clean. Extensive experiments on several
challenging benchmarks demonstrate the superior performance of our method
against the state-of-the-art.
