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

arXiv: https://arxiv.org/abs/1806.10050 · HTML: https://mlsanity.com/paper/1806.10050/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation](https://mlsanity.com/paper/2107.08751/) (2021)
- [Q-space Guided Collaborative Attention Translation Network for Flexible Diffusion-Weighted Images Synthesis](https://mlsanity.com/paper/2505.09323/) (2025)
- [Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks](https://mlsanity.com/paper/2105.07621/) (2021)

## Abstract (arXiv)

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.