  Learning with Noisy Labels (LNL) has become an appealing topic, as
imperfectly annotated data are relatively cheaper to obtain. Recent
state-of-the-art approaches employ specific selection mechanisms to separate
clean and noisy samples and then apply Semi-Supervised Learning (SSL)
techniques for improved performance. However, the selection step mostly
provides a medium-sized and decent-enough clean subset, which overlooks a rich
set of clean samples. To fulfill this, we propose a novel LNL framework ProMix
that attempts to maximize the utility of clean samples for boosted performance.
Key to our method, we propose a matched high confidence selection technique
that selects those examples with high confidence scores and matched predictions
with given labels to dynamically expand a base clean sample set. To overcome
the potential side effect of excessive clean set selection procedure, we
further devise a novel SSL framework that is able to train balanced and
unbiased classifiers on the separated clean and noisy samples. Extensive
experiments demonstrate that ProMix significantly advances the current
state-of-the-art results on multiple benchmarks with different types and levels
of noise. It achieves an average improvement of 2.48\% on the CIFAR-N dataset.
The code is available at https://github.com/Justherozen/ProMix
