# Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation

arXiv: https://arxiv.org/abs/2008.11882 · HTML: https://mlsanity.com/paper/2008.11882/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Diff-Font: Diffusion Model for Robust One-Shot Font Generation](https://mlsanity.com/paper/2212.05895/) (2022)
- [ReFormer: The Relational Transformer for Image Captioning](https://mlsanity.com/paper/2107.14178/) (2021)
- [Physics-informed Guided Disentanglement in Generative Networks](https://mlsanity.com/paper/2107.14229/) (2021)
- [Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow](https://mlsanity.com/paper/2201.05723/) (2022)
- [Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution](https://mlsanity.com/paper/2202.02779/) (2022)
- [Controllable Image Captioning](https://mlsanity.com/paper/2204.13324/) (2022)
- [Tackling Multiple Tasks with One Single Learning Framework](https://mlsanity.com/paper/2206.06322/) (2022)
- [Visual Transformer for Object Detection](https://mlsanity.com/paper/2206.06323/) (2022)
- [A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction](https://mlsanity.com/paper/2209.09198/) (2022)

## Abstract (arXiv)

State-of-the-art techniques in Generative Adversarial Networks (GANs) have shown remarkable success in image-to-image translation from peer domain X to domain Y using paired image data. However, obtaining abundant paired data is a non-trivial and expensive process in the majority of applications. When there is a need to translate images across n domains, if the training is performed between every two domains, the complexity of the training will increase quadratically. Moreover, training with data from two domains only at a time cannot benefit from data of other domains, which prevents the extraction of more useful features and hinders the progress of this research area. In this work, we propose a general framework for unsupervised image-to-image translation across multiple domains, which can translate images from domain X to any a domain without requiring direct training between the two domains involved in image translation. A byproduct of the framework is the reduction of computing time and computing resources, since it needs less time than training the domains in pairs as is done in state-of-the-art works. Our proposed framework consists of a pair of encoders along with a pair of GANs which learns high-level features across different domains to generate diverse and realistic samples from. Our framework shows competing results on many image-to-image tasks compared with state-of-the-art techniques.