# FairFaceGAN: Fairness-aware Facial Image-to-Image Translation

arXiv: https://arxiv.org/abs/2012.00282 · HTML: https://mlsanity.com/paper/2012.00282/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 21 of 21)

- [Fair Contrastive Learning for Facial Attribute Classification](https://mlsanity.com/paper/2203.16209/) (2022)
- [Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation](https://mlsanity.com/paper/2106.12387/) (2021)
- [Fairness Reprogramming](https://mlsanity.com/paper/2209.10222/) (2022)
- [Fair Generative Models via Transfer Learning](https://mlsanity.com/paper/2212.00926/) (2022)
- [MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining](https://mlsanity.com/paper/2110.08009/) (2021)
- [How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts](https://mlsanity.com/paper/2207.01168/) (2022)
- [Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium](https://mlsanity.com/paper/2410.16432/) (2024)
- [SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles](https://mlsanity.com/paper/2204.05157/) (2022)
- [Distributionally Generative Augmentation for Fair Facial Attribute Classification](https://mlsanity.com/paper/2403.06606/) (2024)
- [Through a fair looking-glass: mitigating bias in image datasets](https://mlsanity.com/paper/2209.08648/) (2022)
- [Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications](https://mlsanity.com/paper/2510.21887/) (2025)
- [On the Fairness Impacts of Private Ensembles Models](https://mlsanity.com/paper/2305.11807/) (2023)
- [Diverse Rare Sample Generation with Pretrained GANs](https://mlsanity.com/paper/2412.19543/) (2024)
- [Distraction is All You Need for Fairness](https://mlsanity.com/paper/2203.07593/) (2022)
- [Fairness in generative modeling](https://mlsanity.com/paper/2210.03517/) (2022)
- [Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets](https://mlsanity.com/paper/2103.09876/) (2021)
- [Adaptation and Generalization for Unknown Sensitive Factors of Variations](https://mlsanity.com/paper/2107.13625/) (2021)
- [SuperStyleNet: Deep Image Synthesis with Superpixel Based Style Encoder](https://mlsanity.com/paper/2112.09367/) (2021)
- [Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and R'enyi Measures](https://mlsanity.com/paper/2206.10043/) (2022)
- [Robustness and Adaptation to Hidden Factors of Variation](https://mlsanity.com/paper/2203.01864/) (2022)
- [Power side-channel leakage localization through adversarial training of deep neural networks](https://mlsanity.com/paper/2410.22425/) (2024)

## Abstract (arXiv)

In this paper, we introduce FairFaceGAN, a fairness-aware facial Image-to-Image translation model, mitigating the problem of unwanted translation in protected attributes (e.g., gender, age, race) during facial attributes editing. Unlike existing models, FairFaceGAN learns fair representations with two separate latents - one related to the target attributes to translate, and the other unrelated to them. This strategy enables FairFaceGAN to separate the information about protected attributes and that of target attributes. It also prevents unwanted translation in protected attributes while target attributes editing. To evaluate the degree of fairness, we perform two types of experiments on CelebA dataset. First, we compare the fairness-aware classification performances when augmenting data by existing image translation methods and FairFaceGAN respectively. Moreover, we propose a new fairness metric, namely Frechet Protected Attribute Distance (FPAD), which measures how well protected attributes are preserved. Experimental results demonstrate that FairFaceGAN shows consistent improvements in terms of fairness over the existing image translation models. Further, we also evaluate image translation performances, where FairFaceGAN shows competitive results, compared to those of existing methods.