Spatially-adaptive normalization (SPADE) is remarkably successful recently in
conditional semantic image synthesis \cite{park2019semantic}, which modulates
the normalized activation with spatially-varying transformations learned from
semantic layouts, to prevent the semantic information from being washed away.
Despite its impressive performance, a more thorough understanding of the
advantages inside the box is still highly demanded to help reduce the
significant computation and parameter overhead introduced by this novel
structure. In this paper, from a return-on-investment point of view, we conduct
an in-depth analysis of the effectiveness of this spatially-adaptive
normalization and observe that its modulation parameters benefit more from
semantic-awareness rather than spatial-adaptiveness, especially for
high-resolution input masks. Inspired by this observation, we propose
class-adaptive normalization (CLADE), a lightweight but equally-effective
variant that is only adaptive to semantic class. In order to further improve
spatial-adaptiveness, we introduce intra-class positional map encoding
calculated from semantic layouts to modulate the normalization parameters of
CLADE and propose a truly spatially-adaptive variant of CLADE, namely
CLADE-ICPE.Through extensive experiments on multiple challenging datasets, we
demonstrate that the proposed CLADE can be generalized to different SPADE-based
methods while achieving comparable generation quality compared to SPADE, but it
is much more efficient with fewer extra parameters and lower computational
cost. The code and pretrained models are available at
https://github.com/tzt101/CLADE.git.

本文从高回报的角度，对空间自适应规范化进行了深入分析，发现其调制参数更加受到语义感知而非空间自适应的影响。在此观察的启发下，本文提出了一种轻量级但同样有效的类自适应规范化变体，称为 CLADE，并介绍了一种计算自语义布局的类内位置图编码，以调制 CLADE 的规范化参数，提出了一种真正的空间自适应变体，即 CLADE-ICPE。通过在多个具有挑战性的数据集上进行大量实验证明，所提出的 CLADE 方法可以推广到不同的基于 SPADE 的方法，并实现与 SPADE 相当的生成质量，而且具有更少的额外参数和更低的计算成本。