Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer vision tasks. While various strategies have been proposed, Mixed Sample Data Augmentation (MSDA) has shown great potential for enhancing model performance and generalization. We introduce a novel mixup method called MiAMix, which stands for Multi-stage Augmented Mixup. MiAMix integrates image augmentation into the mixup framework, utilizes multiple diversified mixing methods concurrently, and improves the mixing method by randomly selecting mixing mask augmentation methods. Recent methods utilize saliency information and the MiAMix is designed for computational efficiency as well, reducing additional overhead and offering easy integration into existing training pipelines. We comprehensively evaluate MiaMix using four image benchmarks and pitting it against current state-of-the-art mixed sample data augmentation techniques to demonstrate that MIAMix improves performance without heavy computational overhead.

通过引入一种名为MiAMix的新的mixup方法，将图像增强与mixup框架相结合，同时利用多种多样的混合方法，并通过随机选择混合掩码增强方法来改进混合方法，该研究表明MiAMix在提高性能的同时降低了计算负载。

MiAMix: 通过多阶段增强混合样本数据增强方法增强图像分类