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Image synthesis is currently one of the most addressed image processing topicin computer vision and deep learning fields of study. Researchers have tackledthis 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 usinga single architecture is a very relevant goal for image generation. In fact, asingle multi-domain network would allow greater flexibility and robustness inthe image synthesis task than other approaches. This paper proposes a novelarchitecture and a training algorithm, which are able to produce multi-domainoutputs using a single network. A small portion of a dataset is intentionallyused, and there are no hard-coded labels (or classes). This is achieved bycombining a conditional Generative Adversarial Network (cGAN) for imagegeneration and a Meta-Learning algorithm for domain switch, and we called ourapproach MetalGAN. The approach has proved to be appropriate for solving themulti-domain problem and it is validated on facial attribute transfer, usingCelebA dataset.

MetalGAN: Multi-Domain Label-Less Image Synthesis Using cGANs and Meta-Learning