  Recent studies on unsupervised image-to-image translation have made a
remarkable progress by training a pair of generative adversarial networks with
a cycle-consistent loss. However, such unsupervised methods may generate
inferior results when the image resolution is high or the two image domains are
of significant appearance differences, such as the translations between
semantic layouts and natural images in the Cityscapes dataset. In this paper,
we propose novel Stacked Cycle-Consistent Adversarial Networks (SCANs) by
decomposing a single translation into multi-stage transformations, which not
only boost the image translation quality but also enable higher resolution
image-to-image translations in a coarse-to-fine manner. Moreover, to properly
exploit the information from the previous stage, an adaptive fusion block is
devised to learn a dynamic integration of the current stage's output and the
previous stage's output. Experiments on multiple datasets demonstrate that our
proposed approach can improve the translation quality compared with previous
single-stage unsupervised methods.
