ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Understand

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.