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

Autor:	Jetchev, Nikolay, Bergmann, Urs, Yildirim, Gokhan
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires compute-intense models parametrized by a huge number of weights, which in turn requires large datasets to make learning successful. Non-parametric exemplar-based generation is a technique that works well to reproduce style from small datasets, but is also compute-intensive. These aspects are a drawback for the practice of digital AI artists: typically one wants to use a small set of stylization images, and needs a fast flexible model in order to experiment with it. With this motivation, our work has these contributions: (i) a novel stylization method called Fully Adversarial Mosaics (FAMOS) that combines the strengths of both parametric and non-parametric approaches; (ii) multiple ablations and image examples that analyze the method and show its capabilities; (iii) source code that will empower artists and machine learning researchers to use and modify FAMOS. Comment: Accepted at the NIPS 2018 workshop on Machine Learning for Creativity and Design
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.09236 Zobrazit plný text záznamu View this record from Arxiv