Abstract
The effectiveness of GANs in producing images according to a specific visual domain has shown potential
in unsupervised domain adaptation. Source labeled images have been modified to mimic target samples for
training classifiers in the target domain, and inverse mappings from the target to the source domain have also been
evaluated, without new image generation.
In this paper we aim at getting the best of both worlds
by introducing a symmetric mapping among domains.
We jointly optimize bi-directional image transformations
combining them with target self-labeling. We define a
new class consistency loss that aligns the generators in
the two directions, imposing to preserve the class identity
of an image passing through both domain mappings. A
detailed analysis of the reconstructed images, a thorough
ablation study and extensive experiments on six different
settings confirm the power of our approach