Training generative adversarial networks (GANs) with limited data generally results in deteriorated performance and collapsed models. To conquer this challenge, we are inspired by the latest observation of Kalibhat et al. (2020); Chen et al.(2021d), that one can discover independently trainable and highly sparse subnetworks (a.k.a., lottery tickets) from GANs. Treating this as an inductive prior, we decompose the data-hungry GAN training into 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 set of 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). Besides, our training framework also displays powerful few-shot generalization ability, i.e., generating high-fidelity images by training from scratch with just 100 real images, without any pre-training. Codes are available at: https://github.com/VITA-Group/Ultra-Data-Efficient-GAN-Training.

本文提出了一种基于罕见子网络和特征增强的数据有效 GAN 训练方法，可应用于多种 GAN 架构和不同数据集。

基于彩票票据视角的数据高效GAN训练（不只是数据增强）