  Every recent image-to-image translation model inherently requires either
image-level (i.e. input-output pairs) or set-level (i.e. domain labels)
supervision. However, even set-level supervision can be a severe bottleneck for
data collection in practice. In this paper, we tackle image-to-image
translation in a fully unsupervised setting, i.e., neither paired images nor
domain labels. To this end, we propose a truly unsupervised image-to-image
translation model (TUNIT) that simultaneously learns to separate image domains
and translates input images into the estimated domains. Experimental results
show that our model achieves comparable or even better performance than the
set-level supervised model trained with full labels, generalizes well on
various datasets, and is robust against the choice of hyperparameters (e.g. the
preset number of pseudo domains). Furthermore, TUNIT can be easily extended to
semi-supervised learning with a few labeled data.
