mlsanityData to 7 Oct 2026

On the Fairness of Generative Adversarial Networks (GANs)

arXiv 2103.00950 ↗First posted 1 Mar 2021cs.LG cs.CVcited by 14

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Abstract (arXiv)

Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. With their ability to directly learn the probability distribution of data, and then sample synthetic realistic data. Many applications have emerged, using GANs to solve classical problems in machine learning, such as data augmentation, class unbalance problems, and fair representation learning. In this paper, we analyze and highlight fairness concerns of GANs model. In this regard, we show empirically that GANs models may inherently prefer certain groups during the training process and therefore they're not able to homogeneously generate data from different groups during the testing phase. Furthermore, we propose solutions to solve this issue by conditioning the GAN model towards samples' group or using ensemble method (boosting) to allow the GAN model to leverage distributed structure of data during the training phase and generate groups at equal rate during the testing phase.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 19 of its 32 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

19 of 32 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  4. 2014Conditional Generative Adversarial Netscited by 2,152
  5. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  6. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  7. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  8. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  9. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  10. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  11. 2019Mode Seeking Generative Adversarial Networks for Diverse Image Synthesiscited by 138
  12. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
7 more
  1. 2017Long Text Generation via Adversarial Training with Leaked Informationcited by 100
  2. 2018BAGAN: Data Augmentation with Balancing GANcited by 90
  3. 2019Data Augmentation Using GANscited by 58
  4. 2016Stacked Generative Adversarial Networkscited by 44
  5. 2019Learning Fair Representations via an Adversarial Frameworkcited by 29
  6. 2020Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillationcited by 18
  7. 2017Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networkscited by 12

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Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 4; 2025: 2.

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  1. 2023Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Modelscited by 297
  2. 2024Fairness Feedback Loops: Training on Synthetic Data Amplifies Biascited by 55
  3. 2024Fair Sampling in Diffusion Models through Switching Mechanismcited by 12
  4. 2022RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clippingcited by 9
  5. 2022Improved Input Reprogramming for GAN Conditioningcited by 7
  6. 2025Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Modelcited by 5
  7. 2021Adversarial Stacked Auto-Encoders for Fair Representation Learningcited by 3
  8. 2022Fairness in generative modelingcited by 2
  9. 2023Balanced Face Dataset: Guiding StyleGAN to Generate Labeled Synthetic Face Image Dataset for Underrepresented Groupcited by 2
  10. 2024Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learningcited by 2
  11. 2022Downstream Fairness Caveats with Synthetic Healthcare Datacited by 1
  12. 2023Fair GANs through model rebalancing for extremely imbalanced class distributionscited by 0
2 more
  1. 2024The Benefits and Risks of Transductive Approaches for AI Fairnesscited by 0
  2. 2025STEB: In Search of the Best Evaluation Approach for Synthetic Time Seriescited by 0