# Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1707.09798 · HTML: https://mlsanity.com/paper/1707.09798/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [An Efficient Style Virtual Try on Network for Clothing Business Industry](https://mlsanity.com/paper/2105.13183/) (2021)

## Abstract (arXiv)

Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because capturing them is often a difficult task. To address the issue, we propose an unsupervised method to learn to transfer visual attribute. The proposed method can learn the transfer function without any corresponding images. Inspecting visualization results from various unsupervised attribute transfer tasks, we verify the effectiveness of the proposed method.