Data Augmentation for Image Classification using Generative AI

Fazle Rahat, M Shifat Hossain, Md Rubel Ahmed, Sumit Kumar Jha, Rickard Ewetz; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4173-4182

Scaling laws dictate that the performance of AI models is proportional to the amount of available data. Data augmentation is a promising solution to expanding the dataset size. Traditional approaches focused on augmentation using rotation translation and resizing. Recent approaches use generative AI models to improve dataset diversity. However the generative methods struggle with issues such as subject corruption and the introduction of irrelevant artifacts. In this paper we propose the Automated Generative Data Augmentation (AGA). The framework combines the utility of large language models (LLMs) diffusion models and segmentation models to augment data. AGA preserves foreground authenticity while ensuring background diversity. Specific contributions include: i) segment and superclass based object extraction ii) prompt diversity with combinatorial complexity using prompt decomposition and iii) affine subject manipulation. We evaluate AGA against state-of-the-art (SOTA) techniques on three representative datasets ImageNet CUB and iWildCam. The experimental evaluation demonstrates an accuracy improvement of 15.6% and 23.5% for in and out-of-distribution data compared to baseline models respectively. There is also 64.3% improvement in SIC score compared to the baselines.