  Training Generative Adversarial Networks (GAN) on high-fidelity images
usually requires large-scale GPU-clusters and a vast number of training images.
In this paper, we study the few-shot image synthesis task for GAN with minimum
computing cost. We propose a light-weight GAN structure that gains superior
quality on 1024*1024 resolution. Notably, the model converges from scratch with
just a few hours of training on a single RTX-2080 GPU, and has a consistent
performance, even with less than 100 training samples. Two technique designs
constitute our work, a skip-layer channel-wise excitation module and a
self-supervised discriminator trained as a feature-encoder. With thirteen
datasets covering a wide variety of image domains (The datasets and code are
available at: https://github.com/odegeasslbc/FastGAN-pytorch), we show our
model's superior performance compared to the state-of-the-art StyleGAN2, when
data and computing budget are limited.
