Explicit Use of Fourier Spectrum in Generative Adversarial Networks
- URL: http://arxiv.org/abs/2208.01265v1
- Date: Tue, 2 Aug 2022 06:26:44 GMT
- Title: Explicit Use of Fourier Spectrum in Generative Adversarial Networks
- Authors: Soroush Sheikh Gargar
- Abstract summary: We show that there is a dissimilarity between the spectrum of authentic images and fake ones.
We propose a new model to reduce the discrepancies between the spectrum of the actual and fake images.
We experimentally show promising improvements in the quality of the generated images.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative Adversarial Networks have got the researchers' attention due to
their state-of-the-art performance in generating new images with only a dataset
of the target distribution. It has been shown that there is a dissimilarity
between the spectrum of authentic images and fake ones. Since the Fourier
transform is a bijective mapping, saying that the model has a significant
problem in learning the original distribution is a fair conclusion. In this
work, we investigate the possible reasons for the mentioned drawback in the
architecture and mathematical theory of the current GANs. Then we propose a new
model to reduce the discrepancies between the spectrum of the actual and fake
images. To that end, we design a brand new architecture for the frequency
domain using the blueprint of geometric deep learning. Then, we experimentally
show promising improvements in the quality of the generated images by
considering the Fourier domain representation of the original data as a
principal feature in the training process.
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