  Sketches reflect the drawing style of individual artists; therefore, it is
important to consider their unique styles when extracting sketches from color
images for various applications. Unfortunately, most existing sketch extraction
methods are designed to extract sketches of a single style. Although there have
been some attempts to generate various style sketches, the methods generally
suffer from two limitations: low quality results and difficulty in training the
model due to the requirement of a paired dataset. In this paper, we propose a
novel multi-modal sketch extraction method that can imitate the style of a
given reference sketch with unpaired data training in a semi-supervised manner.
Our method outperforms state-of-the-art sketch extraction methods and unpaired
image translation methods in both quantitative and qualitative evaluations.
