  This paper observes that there is an issue of high frequencies missing in the
discriminator of standard GAN, and we reveal it stems from downsampling layers
employed in the network architecture. This issue makes the generator lack the
incentive from the discriminator to learn high-frequency content of data,
resulting in a significant spectrum discrepancy between generated images and
real images. Since the Fourier transform is a bijective mapping, we argue that
reducing this spectrum discrepancy would boost the performance of GANs. To this
end, we introduce SSD-GAN, an enhancement of GANs to alleviate the spectral
information loss in the discriminator. Specifically, we propose to embed a
frequency-aware classifier into the discriminator to measure the realness of
the input in both the spatial and spectral domains. With the enhanced
discriminator, the generator of SSD-GAN is encouraged to learn high-frequency
content of real data and generate exact details. The proposed method is general
and can be easily integrated into most existing GANs framework without
excessive cost. The effectiveness of SSD-GAN is validated on various network
architectures, objective functions, and datasets. Code will be available at
https://github.com/cyq373/SSD-GAN.
