  We propose a novel method for unsupervised image-to-image translation, which
incorporates a new attention module and a new learnable normalization function
in an end-to-end manner. The attention module guides our model to focus on more
important regions distinguishing between source and target domains based on the
attention map obtained by the auxiliary classifier. Unlike previous
attention-based method which cannot handle the geometric changes between
domains, our model can translate both images requiring holistic changes and
images requiring large shape changes. Moreover, our new AdaLIN (Adaptive
Layer-Instance Normalization) function helps our attention-guided model to
flexibly control the amount of change in shape and texture by learned
parameters depending on datasets. Experimental results show the superiority of
the proposed method compared to the existing state-of-the-art models with a
fixed network architecture and hyper-parameters. Our code and datasets are
available at https://github.com/taki0112/UGATIT or
https://github.com/znxlwm/UGATIT-pytorch.
