Multi Cycle Consistent Adversarial Networks for Multi Attribute Image to Image Translation

Joseokhee (조석희); Kyu-Cheol Cho (조규철)

doi:10.9708/jksci.2020.25.09.063

Multi Cycle Consistent Adversarial Networks for Multi Attribute Image to Image Translation

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(9), pp.63-69
DOI : 10.9708/jksci.2020.25.09.063
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : June 23, 2020
Accepted : August 25, 2020
Published : September 29, 2020

Joseokhee ¹, Kyu-Cheol Cho ²

¹서강대학교
²인하공업전문대학

Accredited

ABSTRACT

Image-image translation is a technology that creates a target image through input images, and has recently shown high performance in creating a more realistic image by utilizing GAN, which is a non-map learning structure. Therefore, there are various studies on image-to-image translation using GAN. At this point, most image-to-image translations basically target one attribute translation. But the data used and obtainable in real life consist of a variety of features that are hard to explain with one feature. Therefore, if you aim to change multiple attributes that can divide the image creation process by attributes to take advantage of the various attributes, you will be able to play a better role in image-to-image translation. In this paper, we propose Multi CycleGAN, a dual attribute transformation structure, by utilizing CycleGAN, which showed high performance among image-image translation structures using GAN. This structure implements a dual transformation structure in which three domains conduct two-way learning to learn about the two properties of an input domain. Experiments have shown that images through the new structure maintain the properties of the input area and show high performance with the target properties applied. Using this structure, it is possible to create more diverse images in the future, so we can expect to utilize image generation in more diverse areas.

KEYWORDS

image to image translation, GAN, Multi attribute translation, CycleGAN

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* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Multi Cycle Consistent Adversarial Networks for Multi Attribute Image to Image Translation

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Multi Cycle Consistent Adversarial Networks for Multi Attribute Image to Image Translation

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.