Multi-Mapping Image-to-Image Translation with Central Biasing Normalization
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by
Xiaoming Yu, Zhenqiang Ying, Thomas Li, Shan Liu, Ge Li
2020
Abstract
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.
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