Recent advances in image-to-image translation have seen a rise in approaches
generating diverse images through a single network. To indicate the target
domain for a one-to-many mapping, the latent code is injected into the
generator network. However, we found that the injection method leads to mode
collapse because of normalization strategies. Existing normalization strategies
might either cause the inconsistency of feature distribution or eliminate the
effect of the latent code. To solve these problems, we propose the consistency
within diversity criteria for designing the multi-mapping model. Based on the
criteria, we propose central biasing normalization to inject the latent code
information. Experiments show that our method can improve the quality and
diversity of existing image-to-image translation models, such as StarGAN,
BicycleGAN, and pix2pix.

本文提出了一种基于一致性和多样性标准设计的多映射模型，通过集中偏置归一化来解决植入潜在代码信息时出现的问题，实现了在改善现有图像转换模型质量和多样性方面的良好效果。