  In this paper, a new task is proposed, namely, weather translation, which
refers to transferring weather conditions of the image from one category to
another. It is important for photographic style transfer. Although lots of
approaches have been proposed in traditional image translation tasks, few of
them can handle the multi-category weather translation task, since weather
conditions have rich categories and highly complex semantic structures. To
address this problem, we develop a multi-domain weather translation approach
based on generative adversarial networks (GAN), denoted as Weather GAN, which
can achieve the transferring of weather conditions among sunny, cloudy, foggy,
rainy and snowy. Specifically, the weather conditions in the image are
determined by various weather-cues, such as cloud, blue sky, wet ground, etc.
Therefore, it is essential for weather translation to focus the main attention
on weather-cues. To this end, the generator of Weather GAN is composed of an
initial translation module, an attention module and a weather-cue segmentation
module. The initial translation module performs global translation during
generation procedure. The weather-cue segmentation module identifies the
structure and exact distribution of weather-cues. The attention module learns
to focus on the interesting areas of the image while keeping other areas
unaltered. The final generated result is synthesized by these three parts. This
approach suppresses the distortion and deformation caused by weather
translation. our approach outperforms the state-of-the-arts has been shown by a
large number of experiments and evaluations.
