  Image-to-image translation is to map images from a given \emph{style} to
another given \emph{style}. While exceptionally successful, current methods
assume the availability of training images in both source and target domains,
which does not always hold in practice. Inspired by humans' reasoning
capability of analogy, we propose analogical image translation (AIT). Given
images of two styles in the source domain: $\mathcal{A}$ and
$\mathcal{A}^\prime$, along with images $\mathcal{B}$ of the first style in the
target domain, learn a model to translate $\mathcal{B}$ to $\mathcal{B}^\prime$
in the target domain, such that $\mathcal{A}:\mathcal{A}^\prime
::\mathcal{B}:\mathcal{B}^\prime$. AIT is especially useful for translation
scenarios in which training data of one style is hard to obtain but training
data of the same two styles in another domain is available. For instance, in
the case from normal conditions to extreme, rare conditions, obtaining real
training images for the latter case is challenging but obtaining synthetic data
for both cases is relatively easy. In this work, we are interested in adding
adverse weather effects, more specifically fog effects, to images taken in
clear weather. To circumvent the challenge of collecting real foggy images, AIT
learns with synthetic clear-weather images, synthetic foggy images and real
clear-weather images to add fog effects onto real clear-weather images without
seeing any real foggy images during training. AIT achieves this zero-shot image
translation capability by coupling a supervised training scheme in the
synthetic domain, a cycle consistency strategy in the real domain, an
adversarial training scheme between the two domains, and a novel network
design. Experiments show the effectiveness of our method for zero-short image
translation and its benefit for downstream tasks such as semantic foggy scene
understanding.
