  Spatially-adaptive normalization is remarkably successful recently in
conditional semantic image synthesis, which modulates the normalized activation
with spatially-varying transformations learned from semantic layouts, to
preserve the semantic information from being washed away. Despite its
impressive performance, a more thorough understanding of the true advantages
inside the box is still highly demanded, to help reduce the significant
computation and parameter overheads introduced by these new structures. In this
paper, from a return-on-investment point of view, we present a deep analysis of
the effectiveness of SPADE and observe that its advantages actually come mainly
from its semantic-awareness rather than the spatial-adaptiveness. Inspired by
this point, we propose class-adaptive normalization (CLADE), a lightweight
variant that is not adaptive to spatial positions or layouts. Benefited from
this design, CLADE greatly reduces the computation cost while still being able
to preserve the semantic information during the generation. Extensive
experiments on multiple challenging datasets demonstrate that while the
resulting fidelity is on par with SPADE, its overhead is much cheaper than
SPADE. Take the generator for ADE20k dataset as an example, the extra parameter
and computation cost introduced by CLADE are only 4.57% and 0.07% while that of
SPADE are 39.21% and 234.73% respectively.
