Institute of Computing Technology, Chinese Academy IR
Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation | |
Mao, Fengling1,2; Ma, Bingpeng3; Chang, Hong2,3; Shan, Shiguang2,3,4; Chen, Xilin2,3 | |
2021 | |
发表期刊 | SCIENCE CHINA-INFORMATION SCIENCES
![]() |
ISSN | 1674-733X |
卷号 | 64期号:2页码:12 |
摘要 | For a given text, previous text-to-image synthesis methods commonly utilize a multistage generation model to produce images with high resolution in a coarse-to-fine manner. However, these methods ignore the interaction among stages, and they do not constrain the consistent cross-sample relations of images generated in different stages. These deficiencies result in inefficient generation and discrimination. In this study, we propose an interstage cross-sample similarity distillation model based on a generative adversarial network (GAN) for learning efficient text-to-image synthesis. To strengthen the interaction among stages, we achieve interstage knowledge distillation from the refined stage to the coarse stages with novel interstage cross-sample similarity distillation blocks. To enhance the constraint on the cross-sample relations of the images generated at different stages, we conduct cross-sample similarity distillation among the stages. Extensive experiments on the Oxford-102 and Caltech-UCSD Birds-200-2011 (CUB) datasets show that our model generates visually pleasing images and achieves quantitatively comparable performance with state-of-the-art methods. |
关键词 | generative adversarial network (GAN) text-to-image synthesis knowledge distillation |
DOI | 10.1007/s11432-020-2900-x |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61876171] ; National Natural Science Foundation of China[61976203] ; Fundamental Research Funds for the Central Universities |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000595379700001 |
出版者 | SCIENCE PRESS |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/15960 |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Ma, Bingpeng |
作者单位 | 1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Chinese Acad Sci CAS, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China |
推荐引用方式 GB/T 7714 | Mao, Fengling,Ma, Bingpeng,Chang, Hong,et al. Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation[J]. SCIENCE CHINA-INFORMATION SCIENCES,2021,64(2):12. |
APA | Mao, Fengling,Ma, Bingpeng,Chang, Hong,Shan, Shiguang,&Chen, Xilin.(2021).Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation.SCIENCE CHINA-INFORMATION SCIENCES,64(2),12. |
MLA | Mao, Fengling,et al."Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation".SCIENCE CHINA-INFORMATION SCIENCES 64.2(2021):12. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论