Before revision:  Treating this as an inductive prior, we decompose the data-hungry GAN traininginto two sequential sub-problems: (i) identifying the lottery ticket from the original GAN ; then (ii) training the found sparse subnetwork with aggressive data and feature augmentations. Both sub-problems re-use the same small training setof real images. 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:  Treating this as an inductive prior, we decompose the data-hungry GAN traininginto two sequential sub-problems: (i) identifying the lottery ticket from the original GAN using the small training set of real images; and then focusing on training that sparse subnetwork by re-using the same set. Both steps have lower complexity and are 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).