  Facial attributes are important since they provide a detailed description and
determine the visual appearance of human faces. In this paper, we aim at
converting a face image to a sketch while simultaneously generating facial
attributes. To this end, we propose a novel Attribute-Guided Sketch Generative
Adversarial Network (ASGAN) which is an end-to-end framework and contains two
pairs of generators and discriminators, one of which is used to generate faces
with attributes while the other one is employed for image-to-sketch
translation. The two generators form a W-shaped network (W-net) and they are
trained jointly with a weight-sharing constraint. Additionally, we also propose
two novel discriminators, the residual one focusing on attribute generation and
the triplex one helping to generate realistic looking sketches. To validate our
model, we have created a new large dataset with 8,804 images, named the
Attribute Face Photo & Sketch (AFPS) dataset which is the first dataset
containing attributes associated to face sketch images. The experimental
results demonstrate that the proposed network (i) generates more
photo-realistic faces with sharper facial attributes than baselines and (ii)
has good generalization capability on different generative tasks.
