Adaptive instance normalization (AdaIN) has become the standard method for
style injection: by re-normalizing features through scale-and-shift operations,
it has found widespread use in style transfer, image generation, and
image-to-image translation. In this work, we present a generalization of AdaIN
which relies on the whitening and coloring transformation (WCT) which we dub
AdaWCT, that we apply for style injection in large GANs. We show, through
experiments on the StarGANv2 architecture, that this generalization, albeit
conceptually simple, results in significant improvements in the quality of the
generated images.Comment: 4 pages + ref