Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation
This work addresses domain gaps in image translation and adaptation for computer vision applications, presenting incremental improvements.
The paper tackles the problem of limited realism in image-to-image translation for scenarios like rain generation and extends it to unsupervised domain adaptation (UDA), achieving on-par performance with state-of-the-art methods using a simpler approach.
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.