GAN Based

Generative Adversarial Networks (GANs) are increasingly used to generate synthetic data, addressing limitations in real-world data acquisition and annotation. Current research focuses on improving GAN architectures, such as incorporating diffusion models and attention mechanisms, to enhance the quality, diversity, and controllability of generated outputs across various domains, including image generation, face manipulation, and data augmentation for imbalanced datasets. This work is significant because it enables advancements in diverse fields like computer vision, medical imaging, and autonomous driving, by providing high-quality synthetic data for training and testing machine learning models where real data is scarce, expensive, or privacy-sensitive.