Improved Algorithm for Fast Arbitrary Style Transfer Meta Network

Abstract

The fast arbitrary style transfer based on meta-networks has attracted great attention and high praise. However, visible gray blocks of stylistic incongruity often appear in stylized result image. The hue of stylized result image is often not consistent with target style image, which severely affects qualities of final transfer results. This paper proposes an improvement strategy. Gram matrix is used as a style statistic for meta network input and loss computing. By integrating the average pooling operation of Gram matrix and the grouped full connection strategy of meta-network, this paper effectively avoids the problem of too large network parameters brought by traditional Gram matrix. Experimental results show that the method can not only effectively eliminate the incongruous style block problem, but also achieve better visual effect than the original method in texture and color layout. Through theoretical analysis and experimental evidence, this paper confirms the superiority of using Gram matrix as style loss and feature statistics on convergence and visual effect.

Keywords

• meta network
• style transfer
• gram matrix
• arbitrary style
• neural network