  The Pix2pix and CycleGAN losses have vastly improved the qualitative and
quantitative visual quality of results in image-to-image translation tasks. We
extend this framework by exploring approximately invertible architectures which
are well suited to these losses. These architectures are approximately
invertible by design and thus partially satisfy cycle-consistency before
training even begins. Furthermore, since invertible architectures have constant
memory complexity in depth, these models can be built arbitrarily deep. We are
able to demonstrate superior quantitative output on the Cityscapes and Maps
datasets at near constant memory budget.
