用学术化的英语说明一下MixUp数据增强方法

MixUp is a data augmentation technique that involves taking two random samples from a dataset, and linearly interpolating between them to create a new sample. The labels for the new sample are also linearly interpolated between the labels of the original samples. This technique has been shown to improve the generalization performance of deep neural networks, by encouraging the model to learn more robust and discriminative features. MixUp has been successfully applied in a variety of computer vision and natural language processing tasks, and is considered to be a state-of-the-art data augmentation method.