C Gan

Conditional Generative Adversarial Networks (C-GANs) are a class of generative models designed to produce outputs conditioned on specific inputs, enabling controlled image synthesis and other data generation tasks. Current research focuses on improving C-GAN architectures, such as incorporating diffusion models or transformers, to enhance stability, detail, and control over generated outputs, addressing challenges like mode collapse and disentanglement. These advancements are impacting diverse fields, including medical imaging (e.g., improving image reconstruction and segmentation), biometrics (e.g., generating realistic face morphs for security testing), and data augmentation for various machine learning applications. The overall goal is to create more robust and versatile generative models capable of producing high-quality, controlled data for a wide range of applications.