  Manipulating human facial images between two domains is an important and
interesting problem. Most of the existing methods address this issue by
applying two generators or one generator with extra conditional inputs. In this
paper, we proposed a novel self-perception method based on GANs for automatical
face attribute inverse. The proposed method takes face images as inputs and
employs only one single generator without being conditioned on other inputs.
Profiting from the multi-loss strategy and modified U-net structure, our model
is quite stable in training and capable of preserving finer details of the
original face images.
