The high capacity of deep learning models to learn complex patterns poses a significant challenge when confronted with label noise. The inability to differentiate clean and noisy labels ultimately results in poor generalization. We approach this problem by reassigning the label for each image using a new teacher-student based framework termed P-LC (pseudo-label correction). Traditional teacher-student networks are composed of teacher and student classifiers for knowledge distillation. In our novel approach, we reconfigure the teacher network into a triple encoder, leveraging the triplet loss to establish a pseudo-label correction system. As the student generates pseudo labels for a set of given images, the teacher learns to choose between the initially assigned labels and the pseudo labels. Experiments on MNIST, Fashion-MNIST, and SVHN demonstrate P-LC's superior performance over existing state-of-the-art methods across all noise levels, most notably in high noise. In addition, we introduce a noise level estimation to help assess model performance and inform the need for additional data cleaning procedures.

翻译：深度学习模型学习复杂模式的高能力使其在面对标签噪声时面临重大挑战。无法区分干净标签和噪声标签最终会导致泛化能力下降。我们通过一种新的师生框架（称为P-LC，即伪标签修正）为每张图像重新分配标签来解决这一问题。传统的师生网络由教师分类器和学生分类器组成，用于知识蒸馏。在我们的新方法中，我们将教师网络重构为三重编码器，利用三元组损失建立伪标签修正系统。当学生为给定的一组图像生成伪标签时，教师学习在初始分配标签和伪标签之间进行选择。在MNIST、Fashion-MNIST和SVHN上的实验表明，P-LC在所有噪声水平下，尤其是在高噪声环境下，表现优于现有最先进的方法。此外，我们引入了一种噪声水平估计方法，以帮助评估模型性能并告知是否需要额外的数据清洗步骤。