Neural Style Transfer With Adaptive Auto-Correlation Alignment Loss

Abstract

The neural style transfer has achieved a significant improvement with deep learning methods. However, the existing methods are susceptible to lack the ability for handling the texture style transfer because of their less consideration of the textural structure from style images. To overcome this drawback, this letter presents a simple method to capture the textural structure by using an adaptive auto-correlation alignment loss function. Furthermore, we also introduce three metrics to quantitatively evaluate the performance. We qualitatively and quantitatively evaluate the proposed methods. The experimental results demonstrate the superiority of the proposed method and our method can synthesize the stylized images with rich texture style patterns.