  Training generative models, such as GANs, on a target domain containing
limited examples (e.g., 10) can easily result in overfitting. In this work, we
seek to utilize a large source domain for pretraining and transfer the
diversity information from source to target. We propose to preserve the
relative similarities and differences between instances in the source via a
novel cross-domain distance consistency loss. To further reduce overfitting, we
present an anchor-based strategy to encourage different levels of realism over
different regions in the latent space. With extensive results in both
photorealistic and non-photorealistic domains, we demonstrate qualitatively and
quantitatively that our few-shot model automatically discovers correspondences
between source and target domains and generates more diverse and realistic
images than previous methods.
