DRMix: Decomposition-Recomposition Data Augmentation with Diffusion Model

Generative models have emerged as powerful tools capable of generating photorealistic images, spawning a wide range of applications across various domains. However, effectively integrating generative models into image classification tasks remains an open problem. Our analysis reveals that current generative data augmentation methods, as well as traditional data augmentation techniques, have limitations in simultaneously ensuring both fidelity (faithful foreground) and diversity (rich background contexts). To address this challenge, we propose Decomposition-Recomposition Data Augmentation (DRMix), an innovative intra-class data augmentation method. DRMix decomposes images into foreground-background and foreground parts, then performs diversified background recomposition and intra-class foreground recomposition, achieving dual diversity enhancement at both the image and part levels, and strikes a better trade-off between fidelity and diversity. Experimental results demonstrate that DRMix significantly improves performance across multiple tasks, including image classification, few-shot learning, and weakly-supervised object localization (WSOL).