  This paper introduces a semi-parametric approach to image inpainting for
irregular holes. The nonparametric part consists of an external image database.
During test time database is used to retrieve a supplementary image, similar to
the input masked picture, and utilize it as auxiliary information for the deep
neural network. Further, we propose a novel method of generating masks with
irregular holes and present public dataset with such masks. Experiments on
CelebA-HQ dataset show that our semi-parametric method yields more realistic
results than previous approaches, which is confirmed by the user study.
