  Unsupervised Image-to-Image Translation achieves spectacularly advanced
developments nowadays. However, recent approaches mainly focus on one model
with two domains, which may face heavy burdens with large cost of $O(n^2)$
training time and model parameters, under such a requirement that $n$ domains
are freely transferred to each other in a general setting. To address this
problem, we propose a novel and unified framework named Domain-Bank, which
consists of a global shared auto-encoder and $n$ domain-specific
encoders/decoders, assuming that a universal shared-latent sapce can be
projected. Thus, we yield $O(n)$ complexity in model parameters along with a
huge reduction of the time budgets. Besides the high efficiency, we show the
comparable (or even better) image translation results over state-of-the-arts on
various challenging unsupervised image translation tasks, including face image
translation, fashion-clothes translation and painting style translation. We
also apply the proposed framework to domain adaptation and achieve
state-of-the-art performance on digit benchmark datasets. Further, thanks to
the explicit representation of the domain-specific decoders as well as the
universal shared-latent space, it also enables us to conduct incremental
learning to add a new domain encoder/decoder. Linear combination of different
domains' representations is also obtained by fusing the corresponding decoders.
