  Traditional convolution-based generative adversarial networks synthesize
images based on hierarchical local operations, where long-range dependency
relation is implicitly modeled with a Markov chain. It is still not sufficient
for categories with complicated structures. In this paper, we characterize
long-range dependence with attentive normalization (AN), which is an extension
to traditional instance normalization. Specifically, the input feature map is
softly divided into several regions based on its internal semantic similarity,
which are respectively normalized. It enhances consistency between distant
regions with semantic correspondence. Compared with self-attention GAN, our
attentive normalization does not need to measure the correlation of all
locations, and thus can be directly applied to large-size feature maps without
much computational burden. Extensive experiments on class-conditional image
generation and semantic inpainting verify the efficacy of our proposed module.
