  Image reconstruction and synthesis have witnessed remarkable progress thanks
to the development of generative models. Nonetheless, gaps could still exist
between the real and generated images, especially in the frequency domain. In
this study, we show that narrowing gaps in the frequency domain can ameliorate
image reconstruction and synthesis quality further. We propose a novel focal
frequency loss, which allows a model to adaptively focus on frequency
components that are hard to synthesize by down-weighting the easy ones. This
objective function is complementary to existing spatial losses, offering great
impedance against the loss of important frequency information due to the
inherent bias of neural networks. We demonstrate the versatility and
effectiveness of focal frequency loss to improve popular models, such as VAE,
pix2pix, and SPADE, in both perceptual quality and quantitative performance. We
further show its potential on StyleGAN2.
