Conditional Fast Style Transfer Network

In this paper, we propose a conditional fast neural style transfer network. We extend the network proposed as a fast neural style transfer network by Johnson et al. [1] so that the network can learn multiple styles at the same time. To do that, we add a conditional input which selects a style to be transferred out of the trained styles. In addition, we show that the proposed network can mix multiple styles, although the network is trained with each of the training styles independently. The proposed network can also transfer different styles to the different parts of a given image at the same time, which we call "spatial style transfer". In the experiments, we confirmed that no quality degradation occurred in the multi-style network compared to the single network, and linear-weighted multi-style fusion enabled us to generate various kinds of new styles which are different from the trained single styles.