  Learning with noisy labels has been studied to address incorrect label
annotations in real-world applications. In this paper, we present ChiMera, a
two-stage learning-from-noisy-labels framework based on semi-supervised
learning, developed based on a novel contrastive learning technique MixCLR. The
key idea of MixCLR is to learn and refine the representations of mixed
augmentations from two different images to better resist label noise. ChiMera
jointly learns the representations of the original data distribution and
mixed-up data distribution via MixCLR, introducing many additional augmented
samples to fill in the gap between different classes. This results in a more
smoothed representation space learned by contrastive learning with better
alignment and a more robust decision boundary. By exploiting MixCLR, ChiMera
also improves the label diffusion process in the semi-supervised noise recovery
stage and further boosts its ability to diffuse correct label information. We
evaluated ChiMera on seven real-world datasets and obtained state-of-the-art
performance on both symmetric noise and asymmetric noise. Our method opens up
new avenues for using contrastive learning on learning with noisy labels and we
envision MixCLR to be broadly applicable to other applications.
