TY - GEN
T1 - W-Net
T2 - 25th International Conference on Neural Information Processing, ICONIP 2018
AU - Jiang, Haochuan
AU - Yang, Guanyu
AU - Huang, Kaizhu
AU - Zhang, Rui
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Due to the huge category number, the sophisticated combinations of various strokes and radicals, and the free writing or printing styles, generating Chinese characters with diverse styles is always considered as a difficult task. In this paper, an efficient and generalized deep framework, namely, the W-Net, is introduced for the one-shot arbitrary-style Chinese character generation task. Specifically, given a single character (one-shot) with a specific style (e.g., a printed font or hand-writing style), the proposed W-Net model is capable of learning and generating any arbitrary characters sharing the style similar to the given single character. Such appealing property was rarely seen in the literature. We have compared the proposed W-Net framework to many other competitive methods. Experimental results showed the proposed method is significantly superior in the one-shot setting.
AB - Due to the huge category number, the sophisticated combinations of various strokes and radicals, and the free writing or printing styles, generating Chinese characters with diverse styles is always considered as a difficult task. In this paper, an efficient and generalized deep framework, namely, the W-Net, is introduced for the one-shot arbitrary-style Chinese character generation task. Specifically, given a single character (one-shot) with a specific style (e.g., a printed font or hand-writing style), the proposed W-Net model is capable of learning and generating any arbitrary characters sharing the style similar to the given single character. Such appealing property was rarely seen in the literature. We have compared the proposed W-Net framework to many other competitive methods. Experimental results showed the proposed method is significantly superior in the one-shot setting.
UR - http://www.scopus.com/inward/record.url?scp=85059064109&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-04221-9_43
DO - 10.1007/978-3-030-04221-9_43
M3 - Conference Proceeding
AN - SCOPUS:85059064109
SN - 9783030042202
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 483
EP - 493
BT - Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
A2 - Leung, Andrew Chi Sing
A2 - Cheng, Long
A2 - Ozawa, Seiichi
PB - Springer Verlag
Y2 - 13 December 2018 through 16 December 2018
ER -