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Despite substantial progress in the field of deep learning, overfittingpersists as a critical challenge, and data augmentation has emerged as aparticularly promising approach due to its capacity to enhance modelgeneralization in various computer vision tasks. While various strategies havebeen proposed, Mixed Sample Data Augmentation (MSDA) has shown great potentialfor enhancing model performance and generalization. We introduce a novel mixupmethod called MiAMix, which stands for Multi-stage Augmented Mixup. MiAMixintegrates image augmentation into the mixup framework, utilizes multiplediversified mixing methods concurrently, and improves the mixing method byrandomly selecting mixing mask augmentation methods. Recent methods utilizesaliency information and the MiAMix is designed for computational efficiency aswell, reducing additional overhead and offering easy integration into existingtraining pipelines. We comprehensively evaluate MiaMix using four imagebenchmarks and pitting it against current state-of-the-art mixed sample dataaugmentation techniques to demonstrate that MIAMix improves performance withoutheavy computational overhead.

MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method