# AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1707.01400 · HTML: https://mlsanity.com/paper/1707.01400/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2017.

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

- [Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID](https://mlsanity.com/paper/2402.00672/) (2024)
- [Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID](https://mlsanity.com/paper/2504.19244/) (2025)
- [Towards General Modality Translation with Contrastive and Predictive Latent Diffusion Bridge](https://mlsanity.com/paper/2510.20819/) (2025)
- [MFEN:Multi-Frequency Expert Network for Visible-Infrared Person Re-ID](https://mlsanity.com/paper/2606.12051/) (2026)
- [Person Re-identification based on Robust Features in Open-world](https://mlsanity.com/paper/2102.10798/) (2021)
- [3D-MOV: Audio-Visual LSTM Autoencoder for 3D Reconstruction of Multiple Objects from Video](https://mlsanity.com/paper/2110.02404/) (2021)
- [Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2509.11587/) (2025)

## Abstract (arXiv)

Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-domain images. One of the methods is to use conditional GAN for alignment. However, previous attempts of adopting conditional GAN do not perform as well as other methods. In this work we present an approach for improving the capability of the methods which are based on conditional GAN. We evaluate the proposed method on numerous tasks and the experimental results show that it is able to align the cross-domain images successfully in absence of paired samples. Furthermore, we also propose another model which conditions on multiple information such as domain information and label information. Conditioning on domain information and label information, we are able to conduct label propagation from the source domain to the target domain. A 2-step alternating training algorithm is proposed to learn this model.