# Multi-mapping Image-to-Image Translation via Learning Disentanglement

arXiv: https://arxiv.org/abs/1909.07877 · HTML: https://mlsanity.com/paper/1909.07877/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 30 of 35)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Image-to-image Translation via Hierarchical Style Disentanglement](https://mlsanity.com/paper/2103.01456/) (2021)
- [Neural Texture Extraction and Distribution for Controllable Person Image Synthesis](https://mlsanity.com/paper/2204.06160/) (2022)
- [Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts](https://mlsanity.com/paper/2104.00887/) (2021)
- [Image Synthesis via Semantic Composition](https://mlsanity.com/paper/2109.07053/) (2021)
- [Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation](https://mlsanity.com/paper/2106.13292/) (2021)
- [Few-Shot Font Generation by Learning Fine-Grained Local Styles](https://mlsanity.com/paper/2205.09965/) (2022)
- [FlexIT: Towards Flexible Semantic Image Translation](https://mlsanity.com/paper/2203.04705/) (2022)
- [Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis](https://mlsanity.com/paper/2304.10278/) (2023)
- [GAN-based Facial Attribute Manipulation](https://mlsanity.com/paper/2210.12683/) (2022)
- [Scaling-up Disentanglement for Image Translation](https://mlsanity.com/paper/2103.14017/) (2021)
- [Unaligned Image-to-Image Translation by Learning to Reweight](https://mlsanity.com/paper/2109.11736/) (2021)
- [Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data](https://mlsanity.com/paper/2203.10474/) (2022)
- [Physics-informed Guided Disentanglement in Generative Networks](https://mlsanity.com/paper/2107.14229/) (2021)
- [Combining Attention with Flow for Person Image Synthesis](https://mlsanity.com/paper/2108.01823/) (2021)
- [Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2203.12707/) (2022)
- [TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets](https://mlsanity.com/paper/2105.06219/) (2021)
- [Few-shot Font Generation with Weakly Supervised Localized Representations](https://mlsanity.com/paper/2112.11895/) (2021)
- [ManiFest: Manifold Deformation for Few-shot Image Translation](https://mlsanity.com/paper/2111.13681/) (2021)
- [In-Domain GAN Inversion for Faithful Reconstruction and Editability](https://mlsanity.com/paper/2309.13956/) (2023)
- [Few-shot Font Generation by Learning Style Difference and Similarity](https://mlsanity.com/paper/2301.10008/) (2023)
- [Density-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement](https://mlsanity.com/paper/2103.06501/) (2021)
- [Evaluation of Correctness in Unsupervised Many-to-Many Image Translation](https://mlsanity.com/paper/2103.15727/) (2021)
- [Multimodal Face Synthesis from Visual Attributes](https://mlsanity.com/paper/2104.04362/) (2021)
- [SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing](https://mlsanity.com/paper/2112.00180/) (2021)
- [Contrastive Monotonic Pixel-Level Modulation](https://mlsanity.com/paper/2207.11517/) (2022)
- [Smooth image-to-image translations with latent space interpolations](https://mlsanity.com/paper/2210.00841/) (2022)
- [3D-Aware Multi-Class Image-to-Image Translation with NeRFs](https://mlsanity.com/paper/2303.15012/) (2023)
- [Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation](https://mlsanity.com/paper/2110.05055/) (2021)
- [Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing](https://mlsanity.com/paper/2110.11159/) (2021)

## Abstract (arXiv)

Recent advances of image-to-image translation focus on learning the one-to-many mapping from two aspects: multi-modal translation and multi-domain translation. However, the existing methods only consider one of the two perspectives, which makes them unable to solve each other's problem. To address this issue, we propose a novel unified model, which bridges these two objectives. First, we disentangle the input images into the latent representations by an encoder-decoder architecture with a conditional adversarial training in the feature space. Then, we encourage the generator to learn multi-mappings by a random cross-domain translation. As a result, we can manipulate different parts of the latent representations to perform multi-modal and multi-domain translations simultaneously. Experiments demonstrate that our method outperforms state-of-the-art methods.