  This paper proposes a novel approach to performing image-to-image translation
between unpaired domains. Rather than relying on a cycle constraint, our method
takes advantage of collaboration between various GANs. This results in a
multi-modal method, in which multiple optional and diverse images are produced
for a given image. Our model addresses some of the shortcomings of classical
GANs: (1) It is able to remove large objects, such as glasses. (2) Since it
does not need to support the cycle constraint, no irrelevant traces of the
input are left on the generated image. (3) It manages to translate between
domains that require large shape modifications. Our results are shown to
outperform those generated by state-of-the-art methods for several challenging
applications on commonly-used datasets, both qualitatively and quantitatively.
