Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation
2021
发表期刊SCIENCE CHINA-INFORMATION SCIENCES (IF:7.3[JCR-2023],5.8[5-Year])
ISSN1674-733X
EISSN1869-1919
卷号64期号:2
发表状态已发表
DOI10.1007/s11432-020-2900-x
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
收录类别EI ; SCIE
语种英语
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
原始文献类型Article
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/127904
专题信息科学与技术学院_硕士生
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
第一作者单位信息科学与技术学院
第一作者的第一单位信息科学与技术学院
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).
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).
MLA Mao, Fengling,et al."Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation".SCIENCE CHINA-INFORMATION SCIENCES 64.2(2021).
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