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Self-Attention Generative Adversarial Networks

Self-Attention Generative Adversarial NetworksHan Zhang1 2 Ian Goodfellow2 Dimitris Metaxas1 Augustus Odena2 AbstractIn this paper, we propose the Self-Attention Gen-erative Adversarial Network (SAGAN) whichallows attention -driven, long-range dependencymodeling for image generation tasks. Traditionalconvolutional GANs generate high-resolution de-tails as a function of only spatially local pointsin lower-resolution feature maps. In SAGAN, de-tails can be generated using cues from all featurelocations. Moreover, the discriminator can checkthat highly detailed features in distant portionsof the image are consistent with each other. Fur-thermore, recent work has shown that generatorconditioning affects GAN performance.

forms better than prior work1, boosting the best published Inception score from 36.8 to 52.52 and reducing Fr´echet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visu-alization of the attention layers shows that the gen-erator leverages neighborhoods that correspond to object shapes rather than local regions of fixed

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