  One of the attractive characteristics of deep neural networks is their
ability to transfer knowledge obtained in one domain to other related domains.
As a result, high-quality networks can be trained in domains with relatively
little training data. This property has been extensively studied for
discriminative networks but has received significantly less attention for
generative models. Given the often enormous effort required to train GANs, both
computationally as well as in the dataset collection, the re-use of pretrained
GANs is a desirable objective. We propose a novel knowledge transfer method for
generative models based on mining the knowledge that is most beneficial to a
specific target domain, either from a single or multiple pretrained GANs. This
is done using a miner network that identifies which part of the generative
distribution of each pretrained GAN outputs samples closest to the target
domain. Mining effectively steers GAN sampling towards suitable regions of the
latent space, which facilitates the posterior finetuning and avoids pathologies
of other methods such as mode collapse and lack of flexibility. We perform
experiments on several complex datasets using various GAN architectures
(BigGAN, Progressive GAN) and show that the proposed method, called MineGAN,
effectively transfers knowledge to domains with few target images,
outperforming existing methods. In addition, MineGAN can successfully transfer
knowledge from multiple pretrained GANs. Our code is available at:
https://github.com/yaxingwang/MineGAN.
