  Recent GAN-based architectures have been able to deliver impressive
performance on the general task of image-to-image translation. In particular,
it was shown that a wide variety of image translation operators may be learned
from two image sets, containing images from two different domains, without
establishing an explicit pairing between the images. This was made possible by
introducing clever regularizers to overcome the under-constrained nature of the
unpaired translation problem. In this work, we introduce a novel architecture
for unpaired image translation, and explore several new regularizers enabled by
it. Specifically, our architecture comprises a pair of GANs, as well as a pair
of translators between their respective latent spaces. These cross-translators
enable us to impose several regularizing constraints on the learnt image
translation operator, collectively referred to as latent cross-consistency. Our
results show that our proposed architecture and latent cross-consistency
constraints are able to outperform the existing state-of-the-art on a variety
of image translation tasks.
