  In this paper, we propose the Self-Attention Generative Adversarial Network
(SAGAN) which allows attention-driven, long-range dependency modeling for image
generation tasks. Traditional convolutional GANs generate high-resolution
details as a function of only spatially local points in lower-resolution
feature maps. In SAGAN, details can be generated using cues from all feature
locations. Moreover, the discriminator can check that highly detailed features
in distant portions of the image are consistent with each other. Furthermore,
recent work has shown that generator conditioning affects GAN performance.
Leveraging this insight, we apply spectral normalization to the GAN generator
and find that this improves training dynamics. The proposed SAGAN achieves the
state-of-the-art results, boosting the best published Inception score from 36.8
to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the
challenging ImageNet dataset. Visualization of the attention layers shows that
the generator leverages neighborhoods that correspond to object shapes rather
than local regions of fixed shape.
