  As an important and challenging problem, few-shot image generation aims at
generating realistic images through training a GAN model given few samples. A
typical solution for few-shot generation is to transfer a well-trained GAN
model from a data-rich source domain to the data-deficient target domain. In
this paper, we propose a novel self-supervised transfer scheme termed D3T-GAN,
addressing the cross-domain GANs transfer in few-shot image generation.
Specifically, we design two individual strategies to transfer knowledge between
generators and discriminators, respectively. To transfer knowledge between
generators, we conduct a data-dependent transformation, which projects and
reconstructs the target samples into the source generator space. Then, we
perform knowledge transfer from transformed samples to generated samples. To
transfer knowledge between discriminators, we design a multi-level discriminant
knowledge distillation from the source discriminator to the target
discriminator on both the real and fake samples. Extensive experiments show
that our method improve the quality of generated images and achieves the
state-of-the-art FID scores on commonly used datasets.
