  In this paper, we revisit the Image-to-Image (I2I) translation problem with
transition consistency, namely the consistency defined on the conditional data
mapping between each data pairs. Explicitly parameterizing each data mappings
with a transition variable $t$, i.e., $x \overset{t(x,y)}{\mapsto}y$, we
discover that existing I2I translation models mainly focus on maintaining
consistency on results, e.g., image reconstruction or attribute prediction,
named result consistency in our paper. This restricts their generalization
ability to generate satisfactory results with unseen transitions in the test
phase. Consequently, we propose to enforce both result consistency and
transition consistency for I2I translation, to benefit the problem with a
closer consistency between the input and output. To benefit the generalization
ability of the translation model, we propose transition encoding to facilitate
explicit regularization of these two {kinds} of consistencies on unseen
transitions. We further generalize such explicitly regularized consistencies to
distribution-level, thus facilitating a generalized overall consistency for I2I
translation problems. With the above design, our proposed model, named
Transition Encoding GAN (TEGAN), can poss superb generalization ability to
generate realistic and semantically consistent translation results with unseen
transitions in the test phase. It also provides a unified understanding of the
existing GAN-based I2I transition models with our explicitly modeling of the
data mapping, i.e., transition. Experiments on four different I2I translation
tasks demonstrate the efficacy and generality of TEGAN.
