Training generative adversarial networks (GANs) on high quality (HQ) images
involves important computing resources. This requirement represents a
bottleneck for the development of applications of GANs. We propose a transfer
learning technique for GANs that significantly reduces training time. Our
approach consists of freezing the low-level layers of both the critic and
generator of the original GAN. We assume an autoencoder constraint in order to
ensure the compatibility of the internal representations of the critic and the
generator. This assumption explains the gain in training time as it enables us
to bypass the low-level layers during the forward and backward passes. We
compare our method to baselines and observe a significant acceleration of the
training. It can reach two orders of magnitude on HQ datasets when compared
with StyleGAN. We prove rigorously, within the framework of optimal transport,
a theorem ensuring the convergence of the learning of the transferred GAN. We
moreover provide a precise bound for the convergence of the training in terms
of the distance between the source and target dataset.

该论文提出了一种基于迁移学习和自编码约束的生成对抗网络的训练技巧，可以极大加速高质量图像的训练，并在最优传输的框架下证明了该方法的收敛性和训练距离的精确界限。