Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation  

作　　者：Fengling MAO Bingpeng MA Hong CHANG Shiguang SHAN Xilin CHEN 

机构地区：[1]School of Information Science and Technology,ShanghaiTech University,Shanghai 201210,China [2]Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS),Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China [3]University of Chinese Academy of Sciences,Beijing 100049,China [4]CAS Center for Excellence in Brain Science and Intelligence Technology,Shanghai 200031,China

出　　处：《Science China(Information Sciences)》2021年第2期15-26,共12页中国科学（信息科学）（英文版）

基　　金：supported in part by National Natural Science Foundation of China(Grant Nos.61876171,61976203);Fundamental Research Funds for the Central Universities。

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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]