Hybrid GAN

Hybrid GANs combine generative adversarial networks (GANs) with other machine learning models, such as variational autoencoders (VAEs) or kernel ridge regression, to improve image generation, data augmentation, and prediction tasks. Current research focuses on developing hybrid architectures tailored to specific applications, including medical image synthesis (e.g., MRI reconstruction), disease diagnosis from medical images, and urban planning through the prediction of transportation indices. These advancements demonstrate the power of hybrid GANs to address challenges in data scarcity, improve model interpretability, and provide valuable insights in diverse fields, leading to more accurate and efficient solutions.