  Unsupervised image-to-image translation is a central task in computer vision.
Current translation frameworks will abandon the discriminator once the training
process is completed. This paper contends a novel role of the discriminator by
reusing it for encoding the images of the target domain. The proposed
architecture, termed as NICE-GAN, exhibits two advantageous patterns over
previous approaches: First, it is more compact since no independent encoding
component is required; Second, this plug-in encoder is directly trained by the
adversary loss, making it more informative and trained more effectively if a
multi-scale discriminator is applied. The main issue in NICE-GAN is the
coupling of translation with discrimination along the encoder, which could
incur training inconsistency when we play the min-max game via GAN. To tackle
this issue, we develop a decoupled training strategy by which the encoder is
only trained when maximizing the adversary loss while keeping frozen otherwise.
Extensive experiments on four popular benchmarks demonstrate the superior
performance of NICE-GAN over state-of-the-art methods in terms of FID, KID, and
also human preference. Comprehensive ablation studies are also carried out to
isolate the validity of each proposed component. Our codes are available at
https://github.com/alpc91/NICE-GAN-pytorch.
