Optimization of attention map based model for improving the usability of style transfer techniques (스타일 전이 기술의 사용성 향상을 위한 attention map 기반 모델 최적화)

Jung-Hye Min (민정혜)

doi:10.9708/jksci.2023.28.08.031

Optimization of attention map based model for improving the usability of style transfer techniques

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2023, 28(8), pp.31-38
DOI : 10.9708/jksci.2023.28.08.031
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 12, 2023
Accepted : August 1, 2023
Published : August 31, 2023

Jung-Hye Min ¹

¹인하공업전문대학

Accredited

ABSTRACT

Style transfer is one of deep learning-based image processing techniques that has been actively researched recently. These research efforts have led to significant improvements in the quality of result images. Style transfer is a technology that takes a content image and a style image as inputs and generates a transformed result image by applying the characteristics of the style image to the content image. It is becoming increasingly important in exploiting the diversity of digital content. To improve the usability of style transfer technology, ensuring stable performance is crucial. Recently, in the field of natural language processing, the concept of Transformers has been actively utilized. Attention maps, which forms the basis of Transformers, is also being actively applied and researched in the development of style transfer techniques. In this paper, we analyze the representative techniques SANet and AdaAttN and propose a novel attention map-based structure which can generate improved style transfer results. The results demonstrate that the proposed technique effectively preserves the structure of the content image while applying the characteristics of the style image.

KEYWORDS

Style Transfer, Self Attention, Feed-forward network, Image Processing, Deep Learning

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

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This paper was written with support from the National Research Foundation of Korea.

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

Optimization of attention map based model for improving the usability of style transfer techniques

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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

Optimization of attention map based model for improving the usability of style transfer techniques

ABSTRACT

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