Data Augmentation for Image Classification using Generative AI
This addresses data scarcity in image classification, particularly for domains like wildlife monitoring, but is incremental as it builds on existing generative methods.
The paper tackles the problem of limited data for image classification by proposing an automated generative data augmentation framework that combines LLMs, diffusion, and segmentation models, resulting in accuracy improvements of 15.6% and 23.5% for in and out-of-distribution data, and a 64.3% improvement in SIC score compared to baselines.
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 a 64.3% improvement in SIC score compared to the baselines.