  Image synthesis is currently one of the most addressed image processing topic
in computer vision and deep learning fields of study. Researchers have tackled
this problem focusing their efforts on its several challenging problems, e.g.
image quality and size, domain and pose changing, architecture of the networks,
and so on. Above all, producing images belonging to different domains by using
a single architecture is a very relevant goal for image generation. In fact, a
single multi-domain network would allow greater flexibility and robustness in
the image synthesis task than other approaches. This paper proposes a novel
architecture and a training algorithm, which are able to produce multi-domain
outputs using a single network. A small portion of a dataset is intentionally
used, and there are no hard-coded labels (or classes). This is achieved by
combining a conditional Generative Adversarial Network (cGAN) for image
generation and a Meta-Learning algorithm for domain switch, and we called our
approach MetalGAN. The approach has proved to be appropriate for solving the
multi-domain problem and it is validated on facial attribute transfer, using
CelebA dataset.
