  Training a generative adversarial network (GAN) with limited data has been a
challenging task. A feasible solution is to start with a GAN well-trained on a
large scale source domain and adapt it to the target domain with a few samples,
termed as few shot generative model adaption. However, existing methods are
prone to model overfitting and collapse in extremely few shot setting (less
than 10). To solve this problem, we propose a relaxed spatial structural
alignment method to calibrate the target generative models during the adaption.
We design a cross-domain spatial structural consistency loss comprising the
self-correlation and disturbance correlation consistency loss. It helps align
the spatial structural information between the synthesis image pairs of the
source and target domains. To relax the cross-domain alignment, we compress the
original latent space of generative models to a subspace. Image pairs generated
from the subspace are pulled closer. Qualitative and quantitative experiments
show that our method consistently surpasses the state-of-the-art methods in few
shot setting.
