Analyzing Generative Models for Realistic Data Augmentation across Modalities and Applications

In the emerging era of artificial intelligence, Generative AI models such as Transformer-based models, Generative Adversarial Networks, Diffusion models and Variational Autoencoders offer a powerful paradigm for data augmentation by enabling the generation of artificial data that be like to the original data distribution. This study discusses the use of generative models in place of traditional augmentation techniques and reviewing their history and drawbacks. It explains the workings of GANs and VAEs, and discusses strategies for effectively integrating them into the augmentation pipeline to boost model performance. Both perceptual metrics like Inception Score and Frechet Inception Distance as well as domain-specific metrics like F1-score, IoU and MSE are presented for rigorously evaluating the quality of generated data. Through case studies and experimental results, the paper demonstrates quantitatively how generative augmentation can enhance dataset diversity, prevent overfitting on limited data, and significantly improve metrics across tasks like classification and object detection. It discusses challenges and future scope of generative models for data augmentation.