The Frequency Discrepancy Between Real and Generated Images
The Frequency Discrepancy Between Real and Generated Images
复制标题
真实图像和生成图像之间的频率差异
DOI:
10.1109/access.2021.3100891
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发表时间:
2021
期刊:
影响因子:
3.9
通讯作者:
Huang Sibo
中科院分区:
文献类型:
--
作者:
Wang Yuehui;Cai Liyan;Zhang Dongyu;Huang Sibo
Despite the success of Generative Adversarial Networks (GANs), little work has focused on the discrepancy between real and generated images in frequency domain. In this work, we provide a systematic analysis on this topic. We first demonstrate the general existence of the frequency discrepancy and further perform extensive experiments both on datasets with various frequency distributions and models with different upsampling methods to reveal the sources of the discrepancy. Experimental results show that: resize-convolution is not a perfect alternative to deconvolution, and natural images and unnatural images should be treated separately during training. Based on these studies, we provide some novel solutions to reduce the discrepancy. Finally, we further show the effectiveness of our solutions on Variational Auto Encoders (VAEs). We hope that the community should pay equal attention to the performance of generative models both in spatial and frequency domain.
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DOI:
10.1007/978-3-540-88693-8_59
发表时间:
2008-10
期刊:
--
影响因子:
--
作者:
Weiwei Zhang;Jian Sun-;Xiaoou Tang
通讯作者:
Weiwei Zhang;Jian Sun-;Xiaoou Tang
DOI:
10.1109/cvpr42600.2020.01429
发表时间:
2020-06
期刊:
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子:
--
作者:
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Steven Liu;Tongzhou Wang;David Bau;Jun-Yan Zhu;A. Torralba
DOI:
--
发表时间:
2018-11
期刊:
--
影响因子:
--
作者:
Shengjia Zhao;Hongyu Ren;Arianna Yuan;Jiaming Song;Noah D. Goodman;Stefano Ermon
通讯作者:
Shengjia Zhao;Hongyu Ren;Arianna Yuan;Jiaming Song;Noah D. Goodman;Stefano Ermon
DOI:
--
发表时间:
2018-06
期刊:
--
影响因子:
--
作者:
Mahyar Khayatkhoei;Maneesh Kumar Singh;A. Elgammal
通讯作者:
Mahyar Khayatkhoei;Maneesh Kumar Singh;A. Elgammal
DOI:
--
发表时间:
2009
期刊:
--
影响因子:
--
作者:
A. Krizhevsky
通讯作者:
A. Krizhevsky