AI RESEARCH
Diffusion-Based Data Augmentation for Image Recognition: A Systematic Analysis and Evaluation
arXiv CS.CV
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ArXi:2603.08364v1 Announce Type: new Diffusion-based data augmentation (DiffDA) has emerged as a promising approach to improving classification performance under data scarcity. However, existing works vary significantly in task configurations, model choices, and experimental pipelines, making it difficult to fairly compare methods or assess their effectiveness across different scenarios. Moreover, there remains a lack of systematic understanding of the full DiffDA workflow. In this work, we