Domain Intersection and Domain Difference
This addresses domain adaptation and image synthesis challenges in computer vision, offering a novel approach for content manipulation across domains.
The paper tackles the problem of recovering shared and unique content between two visual domains to enable domain mapping and image generation from domain intersection and union without training samples, achieving superior performance over existing methods in experiments.
We present a method for recovering the shared content between two visual domains as well as the content that is unique to each domain. This allows us to map from one domain to the other, in a way in which the content that is specific for the first domain is removed and the content that is specific for the second is imported from any image in the second domain. In addition, our method enables generation of images from the intersection of the two domains as well as their union, despite having no such samples during training. The method is shown analytically to contain all the sufficient and necessary constraints. It also outperforms the literature methods in an extensive set of experiments. Our code is available at https://github.com/sagiebenaim/DomainIntersectionDifference.