Training with noisy class labels impairs neural networks' generalization
performance. In this context, mixup is a popular regularization technique to
improve training robustness by making memorizing false class labels more
difficult. However, mixup neglects that, typically, multiple annotators, e.g.,
crowdworkers, provide class labels. Therefore, we propose an extension of
mixup, which handles multiple class labels per instance while considering which
class label originates from which annotator. Integrated into our
multi-annotator classification framework annot-mix, it performs superiorly to
eight state-of-the-art approaches on eleven datasets with noisy class labels
provided either by human or simulated annotators. Our code is publicly
available through our repository at this https URL

噪声类标签训练影响神经网络的泛化性能，提出了一种基于多注释者分类框架 annot-mix 的 mixup 扩展方法，可以处理每个实例的多个类标签，并考虑类标签的注释者来源，优于现有的八种方法，并在使用人类或模拟注释者提供的十一个数据集上表现出色。