题名 | GC-GAN: Photo Cartoonization Using Guided Cartoon Generative Adversarial Network |
作者 | |
发表日期 | 2023 |
会议名称 | 32nd International Conference on Artificial Neural Networks |
会议录名称 | Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part V
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会议录编者 | Lazaros Iliadis, Antonios Papaleonidas, Plamen Angelov, Chrisina Jayne |
ISBN | 978-3-031-44191-2 |
ISSN | 0302-9743 |
卷号 | Lecture Notes in Computer Science (LNCS, volume 14258) |
页码 | 65-77 |
会议日期 | September 26–29, 2023 |
会议地点 | Heraklion, Greece |
出版地 | Cham |
出版者 | Springer |
摘要 | Image cartoonization, a subfield of image translation, has gained increasing attention in recent years. However, the overall performance of existing methods is limited by shortcomings such as poorly fitting regions. To address these issues, our paper proposes the Guided Cartoon Generative Adversarial Network (GC-GAN). Our approach introduces a segmentation step before the training process, which splits and guides mixed training images into a human face set and a scenery set. This enables our model to extract features specifically from cartoon faces and generate more realistic results. Furthermore, we include a loss function called triplet loss in our framework, which drives the network to bring output closer to a referenced image and focus more on the detailed parts of the training images. This improves the overall quality of the generated images and addresses the issue of poorly fitting regions. Compared to the state-of-the-art White-box CartoonGAN, our work improves Fréchet Inception Distance by 18.7% and Kernel Inception Distance by 17.6%, respectively. Additionally, our work surpasses AnimeGAN in terms of Fréchet Inception Distance by 40.7% and Kernel Inception Distance by 47.4%. |
关键词 | Generative adversarial network Image cartoonization Image translation |
DOI | 10.1007/978-3-031-44192-9_6 |
URL | 查看来源 |
收录类别 | CPCI-S |
语种 | 英语English |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods |
WOS记录号 | WOS:001156948900006 |
Scopus入藏号 | 2-s2.0-85174633984 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/11673 |
专题 | 理工科技学院 |
通讯作者 | Chen, Donglong |
作者单位 | 1.Guangdong Provincial Key Laboratory IRADS,BNU-HKBU United International College,Zhuhai,China 2.Fudan University,Shanghai,China |
第一作者单位 | 北师香港浸会大学 |
通讯作者单位 | 北师香港浸会大学 |
推荐引用方式 GB/T 7714 | Zhang, Jiachen,Hou, Huantong,Chen, Jingjinget al. GC-GAN: Photo Cartoonization Using Guided Cartoon Generative Adversarial Network[C]//Lazaros Iliadis, Antonios Papaleonidas, Plamen Angelov, Chrisina Jayne. Cham: Springer, 2023: 65-77. |
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