  Because deep learning is vulnerable to noisy labels, sample selection
techniques, which train networks with only clean labeled data, have attracted a
great attention. However, if the labels are dominantly corrupted by few
classes, these noisy samples are called dominant-noisy-labeled samples, the
network also learns dominant-noisy-labeled samples rapidly via content-aware
optimization. In this study, we propose a compelling criteria to penalize
dominant-noisy-labeled samples intensively through class-wise penalty labels.
By averaging prediction confidences for the each observed label, we obtain
suitable penalty labels that have high values if the labels are largely
corrupted by some classes. Experiments were performed using benchmarks
(CIFAR-10, CIFAR-100, Tiny-ImageNet) and real-world datasets (ANIMAL-10N,
Clothing1M) to evaluate the proposed criteria in various scenarios with
different noise rates. Using the proposed sample selection, the learning
process of the network becomes significantly robust to noisy labels compared to
existing methods in several noise types.
