  Image-to-image translation architectures may have limited effectiveness in
some circumstances. For example, while generating rainy scenarios, they may
fail to model typical traits of rain as water drops, and this ultimately
impacts the synthetic images realism. With our method, called domain bridge,
web-crawled data are exploited to reduce the domain gap, leading to the
inclusion of previously ignored elements in the generated images. We make use
of a network for clear to rain translation trained with the domain bridge to
extend our work to Unsupervised Domain Adaptation (UDA). In that context, we
introduce an online multimodal style-sampling strategy, where image translation
multimodality is exploited at training time to improve performances. Finally, a
novel approach for self-supervised learning is presented, and used to further
align the domains. With our contributions, we simultaneously increase the
realism of the generated images, while reaching on par performances with
respect to the UDA state-of-the-art, with a simpler approach.
