# XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings

arXiv: https://arxiv.org/abs/1711.05139 · HTML: https://mlsanity.com/paper/1711.05139/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2017.

## Built on by (most cited first, 27 of 27)

- [Semantic Image Synthesis via Diffusion Models](https://mlsanity.com/paper/2207.00050/) (2022)
- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2108.04547/) (2021)
- [Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation](https://mlsanity.com/paper/2101.06853/) (2021)
- [SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving](https://mlsanity.com/paper/2103.03150/) (2021)
- [Underwater Light Field Retention : Neural Rendering for Underwater Imaging](https://mlsanity.com/paper/2203.11006/) (2022)
- [Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation](https://mlsanity.com/paper/2407.14774/) (2024)
- [StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis](https://mlsanity.com/paper/2104.07098/) (2021)
- [Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation](https://mlsanity.com/paper/2204.02111/) (2022)
- [DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation](https://mlsanity.com/paper/2209.06357/) (2022)
- [Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty](https://mlsanity.com/paper/2101.04442/) (2021)
- [CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training](https://mlsanity.com/paper/2108.11368/) (2021)
- [FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing](https://mlsanity.com/paper/2405.17472/) (2024)
- [Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent space](https://mlsanity.com/paper/2207.09185/) (2022)
- [Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures](https://mlsanity.com/paper/2503.10576/) (2025)
- [IGAN: Inferent and Generative Adversarial Networks](https://mlsanity.com/paper/2109.13360/) (2021)
- [Image Reconstruction via Deep Image Prior Subspaces](https://mlsanity.com/paper/2302.10279/) (2023)
- [Guided Image-to-Image Translation by Discriminator-Generator Communication](https://mlsanity.com/paper/2303.03598/) (2023)
- [CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis](https://mlsanity.com/paper/2505.23444/) (2025)
- [Real-Time Per-Garment Virtual Try-On with Temporal Consistency for Loose-Fitting Garments](https://mlsanity.com/paper/2506.12348/) (2025)
- [Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks](https://mlsanity.com/paper/2101.07389/) (2021)
- [Multi-View Image-to-Image Translation Supervised by 3D Pose](https://mlsanity.com/paper/2104.05779/) (2021)
- [Unified Style Transfer](https://mlsanity.com/paper/2110.10481/) (2021)
- [A Novel Framework for Image-to-image Translation and Image Compression](https://mlsanity.com/paper/2111.13105/) (2021)
- [Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2204.12237/) (2022)
- [Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review](https://mlsanity.com/paper/2303.09012/) (2023)
- [PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow](https://mlsanity.com/paper/2307.09146/) (2023)

## Abstract (arXiv)

Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter. Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce XGAN ("Cross-GAN"), a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions. We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space. We report promising qualitative results for the task of face-to-cartoon translation. The cartoon dataset, CartoonSet, we collected for this purpose is publicly available at google.github.io/cartoonset/ as a new benchmark for semantic style transfer.