Abstract
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.
Abstract (translated)
训练深度神经网络（DNNs）从嘈杂标签是一个重要而具有挑战性的任务。然而，大多数现有方法都关注于嘈杂标签，并忽略了固有数据结构的重要性。为了在嘈杂标签和数据之间搭建一座桥梁，受到物理学中势能概念的启发，我们提出了一个基于势能的噪音标签学习的新模型。我们在其类中心上应用了势能 regularization 的距离基于分类器。将我们所提出的分类器与现有的深度学习骨干嵌入，我们可以获得具有更好特征表示的稳健网络。它们可以保留数据中的固有结构，从而具有卓越的嘈杂容忍性。我们对多个现实世界数据集进行了广泛的实验，以分析我们提出的模型的效率。定量的结果表明，它可以实现最先进的性能。
URL
https://arxiv.org/abs/2405.01186
