Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

Continuous Conditional Generative Adversarial Networks (CcGAN)

Computing the Sliding Fréchet Inception Distance between fake and real images with continous labels

A unified cGAN-based knowledge distillation method

some models with easy understand code

Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable

Code repository for Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinity and Auxiliary Regularization