Before revision:  Such a coordinated framework enables us to focus on lower-complexity and more data-efficient sub-problems, effectively stabilizing training and improving convergence . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).
After revision:  Such a coordinated framework enables us to focus on lower-complexity and more data-efficient to train. We find our coordinated framework to offer orthogonal gains to existing real image data augmentation methods, and we additionally offer a new feature-level augmentation that can be applied together with them . Comprehensive experiments endorse the effectiveness of our proposed ultra-data-efficient training framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN2 ) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet).