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.

翻译：从噪声标签中训练深度神经网络是一项重要且具有挑战性的任务。然而，现有方法大多聚焦于被污染的标签，忽略了内在数据结构的重要性。为弥合噪声标签与数据之间的差距，受物理学中势能概念的启发，我们提出了一种新颖的基于势能的混合模型（PEMM）用于噪声标签学习。我们创新性地设计了一种基于距离的分类器，并在其类中心上施加势能正则化。将所提出的分类器嵌入现有深度学习骨干网络后，我们可以获得具有更优特征表示的鲁棒网络。这些网络能够保留数据的内在结构，从而表现出卓越的噪声容忍性。我们在多个真实数据集上进行了广泛实验以分析所提模型的效率。定量结果表明，该模型能够实现最先进的性能。