@inproceedings{1fd94db7af764cf3adb6afa3d140c06f,
title = "Generalized W-Net: Arbitrary-Style Chinese Character Synthesization",
abstract = "Synthesizing Chinese characters with consistent style using few stylized examples is challenging. Existing models struggle to generate arbitrary style characters with limited examples. In this paper, we propose the Generalized W-Net, a novel class of W-shaped architectures that addresses this. By incorporating Adaptive Instance Normalization and introducing multi-content, our approach can synthesize Chinese characters in any desired style, even with limited examples. It handles seen and unseen styles during training and can generate new character contents. Experimental results demonstrate the effectiveness of our approach.",
author = "Haochuan Jiang and Guanyu Yang and Fei Cheng and Kaizhu Huang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 13th International Conference on Brain Inspired Cognitive Systems, BICS 2023 ; Conference date: 05-08-2023 Through 06-08-2023",
year = "2024",
doi = "10.1007/978-981-97-1417-9_18",
language = "English",
isbn = "9789819714162",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "190--200",
editor = "Jinchang Ren and Amir Hussain and Liao, {Iman Yi} and Rongjun Chen and Kaizhu Huang and Huimin Zhao and Xiaoyong Liu and Ping Ma and Thomas Maul",
booktitle = "Advances in Brain Inspired Cognitive Systems - 13th International Conference, BICS 2023, Proceedings",
}