Conditional GANs

Conditional Generative Adversarial Networks (cGANs) are generative models designed to produce images or signals conditioned on specific inputs, such as text descriptions, labels, or other images. Current research focuses on improving cGAN efficiency (e.g., through pruning and sparse inference), enhancing image quality and control (e.g., via hypernetworks and dual-diffusion methods), and addressing challenges like limited training data and mode collapse. These advancements are significant for various applications, including image-to-image translation, data augmentation, and the synthesis of realistic images for diverse fields like autonomous driving, medical imaging, and satellite imagery analysis.