  An unprecedented booming has been witnessed in the research area of artistic
style transfer ever since Gatys et al. introduced the neural method. One of the
remaining challenges is to balance a trade-off among three critical
aspects---speed, flexibility, and quality: (i) the vanilla optimization-based
algorithm produces impressive results for arbitrary styles, but is
unsatisfyingly slow due to its iterative nature, (ii) the fast approximation
methods based on feed-forward neural networks generate satisfactory artistic
effects but bound to only a limited number of styles, and (iii)
feature-matching methods like AdaIN achieve arbitrary style transfer in a
real-time manner but at a cost of the compromised quality. We find it
considerably difficult to balance the trade-off well merely using a single
feed-forward step and ask, instead, whether there exists an algorithm that
could adapt quickly to any style, while the adapted model maintains high
efficiency and good image quality. Motivated by this idea, we propose a novel
method, coined MetaStyle, which formulates the neural style transfer as a
bilevel optimization problem and combines learning with only a few
post-processing update steps to adapt to a fast approximation model with
satisfying artistic effects, comparable to the optimization-based methods for
an arbitrary style. The qualitative and quantitative analysis in the
experiments demonstrates that the proposed approach achieves high-quality
arbitrary artistic style transfer effectively, with a good trade-off among
speed, flexibility, and quality.
