  Images generated by most of generative models trained with limited data often
exhibit deficiencies in either fidelity, diversity, or both. One effective
solution to address the limitation is few-shot generative model adaption.
However, the type of approaches typically rely on a large-scale pre-trained
model, serving as a source domain, to facilitate information transfer to the
target domain. In this paper, we propose a method called Information Transfer
from the Built Geodesic Surface (ITBGS), which contains two module: Feature
Augmentation on Geodesic Surface (FAGS); Interpolation and Regularization
(I\&R). With the FAGS module, a pseudo-source domain is created by projecting
image features from the training dataset into the Pre-Shape Space, subsequently
generating new features on the Geodesic surface. Thus, no pre-trained models is
needed for the adaption process during the training of generative models with
FAGS. I\&R module are introduced for supervising the interpolated images and
regularizing their relative distances, respectively, to further enhance the
quality of generated images. Through qualitative and quantitative experiments,
we demonstrate that the proposed method consistently achieves optimal or
comparable results across a diverse range of semantically distinct datasets,
even in extremely few-shot scenarios.
