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An unprecedented booming has been witnessed in the research area of artisticstyle transfer ever since Gatys et al. introduced the neural method. One of theremaining challenges is to balance a trade-off among three criticalaspects---speed, flexibility, and quality: (i) the vanilla optimization-basedalgorithm produces impressive results for arbitrary styles, but isunsatisfyingly slow due to its iterative nature, (ii) the fast approximationmethods based on feed-forward neural networks generate satisfactory artisticeffects but bound to only a limited number of styles, and (iii)feature-matching methods like AdaIN achieve arbitrary style transfer in areal-time manner but at a cost of the compromised quality. We find itconsiderably difficult to balance the trade-off well merely using a singlefeed-forward step and ask, instead, whether there exists an algorithm thatcould adapt quickly to any style, while the adapted model maintains highefficiency and good image quality. Motivated by this idea, we propose a novelmethod, coined MetaStyle, which formulates the neural style transfer as abilevel optimization problem and combines learning with only a fewpost-processing update steps to adapt to a fast approximation model withsatisfying artistic effects, comparable to the optimization-based methods foran arbitrary style. The qualitative and quantitative analysis in theexperiments demonstrates that the proposed approach achieves high-qualityarbitrary artistic style transfer effectively, with a good trade-off amongspeed, flexibility, and quality.

MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer