  Training deep neural networks (DNNs) from noisy labels is an important and
challenging task. However, most existing approaches focus on the corrupted
labels and ignore the importance of inherent data structure. To bridge the gap
between noisy labels and data, inspired by the concept of potential energy in
physics, we propose a novel Potential Energy based Mixture Model (PEMM) for
noise-labels learning. We innovate a distance-based classifier with the
potential energy regularization on its class centers. Embedding our proposed
classifier with existing deep learning backbones, we can have robust networks
with better feature representations. They can preserve intrinsic structures
from the data, resulting in a superior noisy tolerance. We conducted extensive
experiments to analyze the efficiency of our proposed model on several
real-world datasets. Quantitative results show that it can achieve
state-of-the-art performance.
