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Parametric generative deep models are state-of-the-art for photo andnon-photo realistic image stylization. However, learning complicated imagerepresentations requires compute-intense models parametrized by a huge numberof weights, which in turn requires large datasets to make learning successful.Non-parametric exemplar-based generation is a technique that works well toreproduce style from small datasets, but is also compute-intensive. Theseaspects are a drawback for the practice of digital AI artists: typically onewants to use a small set of stylization images, and needs a fast flexible modelin order to experiment with it. With this motivation, our work has thesecontributions: (i) a novel stylization method called Fully Adversarial Mosaics(FAMOS) that combines the strengths of both parametric and non-parametricapproaches; (ii) multiple ablations and image examples that analyze the methodand show its capabilities; (iii) source code that will empower artists andmachine learning researchers to use and modify FAMOS.

Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization