# Semi-supervised Image Attribute Editing using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1907.01841 · HTML: https://mlsanity.com/paper/1907.01841/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

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

- [Benchmarking Counterfactual Image Generation](https://mlsanity.com/paper/2403.20287/) (2024)
- [Distributionally Generative Augmentation for Fair Facial Attribute Classification](https://mlsanity.com/paper/2403.06606/) (2024)
- [A comprehensive survey on semantic facial attribute editing using generative adversarial networks](https://mlsanity.com/paper/2205.10587/) (2022)
- [Towards disease-aware image editing of chest X-rays](https://mlsanity.com/paper/2109.01071/) (2021)
- [Evaluation of 3D Counterfactual Brain MRI Generation](https://mlsanity.com/paper/2508.02880/) (2025)
- [Iterative Facial Image Inpainting Based on an Encoder-Generator Architecture](https://mlsanity.com/paper/2101.07036/) (2021)
- [Human Gender Prediction Based on Deep Transfer Learning from Panoramic Radiograph Images](https://mlsanity.com/paper/2205.09850/) (2022)
- [Forensic Dental Age Estimation Using Modified Deep Learning Neural Network](https://mlsanity.com/paper/2208.09799/) (2022)
- [U-Face: An Efficient and Generalizable Framework for Unsupervised Facial Attribute Editing via Subspace Learning](https://mlsanity.com/paper/2603.14004/) (2026)

## Abstract (arXiv)

Image attribute editing is a challenging problem that has been recently studied by many researchers using generative networks. The challenge is in the manipulation of selected attributes of images while preserving the other details. The method to achieve this goal is to find an accurate latent vector representation of an image and a direction corresponding to the attribute. Almost all the works in the literature use labeled datasets in a supervised setting for this purpose. In this study, we introduce an architecture called Cyclic Reverse Generator (CRG), which allows learning the inverse function of the generator accurately via an encoder in an unsupervised setting by utilizing cyclic cost minimization. Attribute editing is then performed using the CRG models for finding desired attribute representations in the latent space. In this work, we use two arbitrary reference images, with and without desired attributes, to compute an attribute direction for editing. We show that the proposed approach performs better in terms of image reconstruction compared to the existing end-to-end generative models both quantitatively and qualitatively. We demonstrate state-of-the-art results on both real images and generated images in CelebA dataset.