Multi-style image transfer system using conditional cycleGAN
學年 109
學期 2
出版（發表）日期 2021-02-01
作品名稱 Multi-style image transfer system using conditional cycleGAN
作品名稱（其他語言）
著者 Ching-Ting Tu; Jen Lin; Yihjia Tsia
單位
出版者
著錄名稱、卷期、頁數 Imaging Science Journal
摘要 This paper aims to extend the capability of Cycle-Consistent Adversarial Network (CycleGAN) by equipping it with a conditional constraint and extend it into a multi-style image transfer system that can transfer images among more than two image domains. The conditional constraint is given in the form of the target style feature map instead of a one-hot vector, and has shown to provide better transfer results. The proposed system offers greater flexibility for users to choose the style for image transfer. Experimental results show that such an architecture is not only feasible but also yields good results. The proposed architecture can be extended to other transformation applications, such as facial expressions transfer, face aging, and synthesis of various features.
關鍵字 Convolutional Neural Network (CNN);deep learning;Generative Adversarial Net (GAN);Conditional GAN (CGAN);CycleGAN;Conditional CycleGAN;PatchGAN;image style transfer
語言 en
ISSN 1368-2199
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者 林慧珍
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/119170 )

SDGS 優質教育,產業創新與基礎設施