Local Mixup

Local Mixup is a data augmentation technique that improves the generalization and robustness of machine learning models by creating synthetic training examples through linear interpolation of existing data points. Current research focuses on refining Mixup's application across various model architectures, including convolutional neural networks, recurrent neural networks, and graph neural networks, exploring variations like selective or local Mixup to address issues such as overfitting and distribution shifts. These advancements are significant because they enhance model performance, particularly in challenging scenarios like whole slide image classification and federated learning, and contribute to a deeper understanding of data augmentation's impact on model generalization and robustness.