Generative Data Augmentation

Generative data augmentation uses artificial intelligence models to create synthetic training data, addressing the limitations of small or biased datasets in various machine learning applications. Current research focuses on leveraging diffusion models, variational autoencoders (VAEs), generative adversarial networks (GANs), and large language models (LLMs) to generate high-quality, diverse, and semantically meaningful synthetic data, often incorporating techniques like classifier-free guidance and controllable generation. This approach significantly impacts fields like medical image analysis, robotics, and natural language processing by improving model performance, particularly in low-resource settings, and enabling more robust and generalizable models.