An Analysis for Image-to-Image Translation and Style Transfer

TL;DR

This paper clarifies the differences and connections between image-to-image translation and style transfer, highlighting their unique concepts, methods, and capabilities to aid the community's understanding and development.

Contribution

It provides a comprehensive analysis distinguishing the two technologies, including concepts, training, evaluation, and visualization, to clarify their respective strengths and limitations.

Findings

Image-to-image translation focuses on domain-based image conversion with strong semantic changes.

Style transfer operates on single images, transferring textures and colors with limited scope.

The paper clarifies the conceptual and practical differences between the two technologies.

Abstract

With the development of generative technologies in deep learning, a large number of image-to-image translation and style transfer models have emerged at an explosive rate in recent years. These two technologies have made significant progress and can generate realistic images. However, many communities tend to confuse the two, because both generate the desired image based on the input image and both cover the two definitions of content and style. In fact, there are indeed significant differences between the two, and there is currently a lack of clear explanations to distinguish the two technologies, which is not conducive to the advancement of technology. We hope to serve the entire community by introducing the differences and connections between image-to-image translation and style transfer. The entire discussion process involves the concepts, forms, training modes, evaluation processes, and visualization results of the two technologies. Finally, we conclude that image-to-image translation divides images by domain, and the types of images in the domain are limited, and the scope involved is small, but the conversion ability is strong and can achieve strong semantic changes. Style transfer divides image types by single image, and the scope involved is large, but the transfer ability is limited, and it transfers more texture and color of the image.