1 Geological Background of Minerlization or Geologic Setting The northeast of Yunnan1 Pb-Zn-Ag-Ge polymetallic ore district is an important part of the southwestern margin of the Yangtze block Sichuan-Yunnan-Guizhou
1 Introduction The Paleogene strata(with a depth of more than 2500m)in the Bohai sea is complex(Xu Changgui,2006),the reservoir buried deeply,the reservoir prediction is difficult(LAI Weicheng,XU Changgui,2012),and more
We propose a systematic analysis of the neglected spectral bias in the frequency domain in this paper.Traditional generative adversarial networks(GANs)try to fulfill the details of images by designing specific network...We propose a systematic analysis of the neglected spectral bias in the frequency domain in this paper.Traditional generative adversarial networks(GANs)try to fulfill the details of images by designing specific network architectures or losses,focusing on generating visually qualitative images.The convolution theorem shows that image processing in the frequency domain is parallelizable and performs better and faster than that in the spatial domain.However,there is little work about discussing the bias of frequency features between the generated images and the real ones.In this paper,we first empirically demonstrate the general distribution bias across datasets and GANs with different sampling methods.Then,we explain the causes of the spectral bias through the deduction that reconsiders the sampling process of the GAN generator.Based on these studies,we provide a low-spectral-bias hybrid generative model to reduce the spectral bias and improve the quality of the generated images.展开更多
文摘1 Geological Background of Minerlization or Geologic Setting The northeast of Yunnan1 Pb-Zn-Ag-Ge polymetallic ore district is an important part of the southwestern margin of the Yangtze block Sichuan-Yunnan-Guizhou
基金funded by Major Projects of National Science and Technology “Large Oil and Gas Fields and CBM development”(Grant No. 2016ZX05 027)
文摘1 Introduction The Paleogene strata(with a depth of more than 2500m)in the Bohai sea is complex(Xu Changgui,2006),the reservoir buried deeply,the reservoir prediction is difficult(LAI Weicheng,XU Changgui,2012),and more
基金supported in part by the National Key Research and Development Program of China under Grant no.2020YFB1806403.
文摘We propose a systematic analysis of the neglected spectral bias in the frequency domain in this paper.Traditional generative adversarial networks(GANs)try to fulfill the details of images by designing specific network architectures or losses,focusing on generating visually qualitative images.The convolution theorem shows that image processing in the frequency domain is parallelizable and performs better and faster than that in the spatial domain.However,there is little work about discussing the bias of frequency features between the generated images and the real ones.In this paper,we first empirically demonstrate the general distribution bias across datasets and GANs with different sampling methods.Then,we explain the causes of the spectral bias through the deduction that reconsiders the sampling process of the GAN generator.Based on these studies,we provide a low-spectral-bias hybrid generative model to reduce the spectral bias and improve the quality of the generated images.
