  The ability to generalize across visual domains is crucial for the robustness
of artificial recognition systems. Although many training sources may be
available in real contexts, the access to even unlabeled target samples cannot
be taken for granted, which makes standard unsupervised domain adaptation
methods inapplicable in the wild. In this work we investigate how to exploit
multiple sources by hallucinating a deep visual domain composed of images,
possibly unrealistic, able to maintain categorical knowledge while discarding
specific source styles. The produced agnostic images are the result of a deep
architecture that applies pixel adaptation on the original source data guided
by two adversarial domain classifier branches at image and feature level. Our
approach is conceived to learn only from source data, but it seamlessly extends
to the use of unlabeled target samples. Remarkable results for both
multi-source domain adaptation and domain generalization support the power of
hallucinating agnostic images in this framework.
