Multi-Mapping Image-to-Image Translation with Central Biasing Normalization

Image-to-image translation is a class of image processing and vision problems that translates an image to a different style or domain. To improve the capacity and performance of one-to-one translation models, multi-mapping image translation have been attempting to extend them for multiple mappings by injecting latent code. Through the analysis of the existing latent code injection models, we find that latent code can determine the target mapping of a generator by controlling the output statistical properties, especially the mean value. However, we find that in some cases the normalization will reduce the consistency of same mapping or the diversity of different mappings. After mathematical analysis, we find the reason behind that is that the distributions of same mapping become inconsistent after batch normalization, and that the effects of latent code are eliminated after instance normalization. To solve these problems, we propose consistency within diversity design criteria for multi-mapping networks. Based on the design criteria, we propose central biasing normalization (CBN) to replace existing latent code injection. CBN can be easily integrated into existing multi-mapping models, significantly reducing model parameters. Experiments show that the results of our method is more stable and diverse than that of existing models. https://github.com/Xiaoming-Yu/cbn .