Conditional GAN
Conditional Generative Adversarial Networks (cGANs) are generative models that synthesize data conditioned on specific input attributes, aiming to create realistic and diverse outputs controlled by these attributes. Current research focuses on improving cGAN training stability, particularly with limited or imbalanced data, exploring novel architectures like those incorporating transformers or incorporating additional discriminators, and developing efficient inference methods for faster generation. This work has significant implications across diverse fields, enabling applications such as data augmentation for improved machine learning model training, realistic data simulation for various scientific domains (e.g., electromagnetic simulations, medical imaging), and enhanced image editing capabilities.