# SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning

arXiv: https://arxiv.org/abs/1810.04991 · HTML: https://mlsanity.com/paper/1810.04991/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation](https://mlsanity.com/paper/2101.05434/) (2021)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [A Domain Gap Aware Generative Adversarial Network for Multi-domain Image Translation](https://mlsanity.com/paper/2110.10837/) (2021)
- [toon2real: Translating Cartoon Images to Realistic Images](https://mlsanity.com/paper/2102.01143/) (2021)
- [Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing](https://mlsanity.com/paper/2110.11159/) (2021)
- [Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks](https://mlsanity.com/paper/2105.07621/) (2021)
- [Effect of Instance Normalization on Fine-Grained Control for Sketch-Based Face Image Generation](https://mlsanity.com/paper/2207.08072/) (2022)

## Abstract (arXiv)

Image translation is a burgeoning field in computer vision where the goal is to learn the mapping between an input image and an output image. However, most recent methods require multiple generators for modeling different domain mappings, which are inefficient and ineffective on some multi-domain image translation tasks. In this paper, we propose a novel method, SingleGAN, to perform multi-domain image-to-image translations with a single generator. We introduce the domain code to explicitly control the different generative tasks and integrate multiple optimization goals to ensure the translation. Experimental results on several unpaired datasets show superior performance of our model in translation between two domains. Besides, we explore variants of SingleGAN for different tasks, including one-to-many domain translation, many-to-many domain translation and one-to-one domain translation with multimodality. The extended experiments show the universality and extensibility of our model.