mlsanityData to 14 Oct 2026

Inclusive GAN: Improving Data and Minority Coverage in Generative Models

arXiv 2004.03355 ↗First posted 7 Apr 2020cs.CVcited by 44

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Abstract (arXiv)

Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images. Yet the equitable allocation of their modeling capacity among subgroups has received less attention, which could lead to potential biases against underrepresented minorities if left uncontrolled. In this work, we first formalize the problem of minority inclusion as one of data coverage, and then propose to improve data coverage by harmonizing adversarial training with reconstructive generation. The experiments show that our method outperforms the existing state-of-the-art methods in terms of data coverage on both seen and unseen data. We develop an extension that allows explicit control over the minority subgroups that the model should ensure to include, and validate its effectiveness at little compromise from the overall performance on the entire dataset. Code, models, and supplemental videos are available at GitHub.

Key facts. Cited by 44 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Apr 2020.

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Citing papers per year: 2021: 15; 2022: 8; 2023: 10; 2024: 8; 2025: 2; 2026: 1.

44 papers.

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  1. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 516
  2. 2021EditGAN: High-Precision Semantic Image Editingcited by 156
  3. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 123
  4. 2021Ensembling Off-the-shelf Models for GAN Trainingcited by 86
  5. 2023ITI-GEN: Inclusive Text-to-Image Generationcited by 64
  6. 2021Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergencecited by 54
  7. 2024Balancing Act: Distribution-Guided Debiasing in Diffusion Modelscited by 52
  8. 2023Analyzing Bias in Diffusion-based Face Generation Modelscited by 46
  9. 2021Beyond the Spectrum: Detecting Deepfakes via Re-Synthesiscited by 44
  10. 2022Self-Distilled StyleGAN: Towards Generation from Internet Photoscited by 37
  11. 2023Don't Play Favorites: Minority Guidance for Diffusion Modelscited by 37
  12. 2021Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Imagescited by 35
32 more
  1. 2021Fairness for Image Generation with Uncertain Sensitive Attributescited by 31
  2. 2021Dual Contrastive Loss and Attention for GANscited by 30
  3. 2021Copyright in Generative Deep Learningcited by 29
  4. 2024Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidancecited by 24
  5. 2023ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentationcited by 20
  6. 2022Generalized One-shot Domain Adaptation of Generative Adversarial Networkscited by 19
  7. 2024Self-Guided Generation of Minority Samples Using Diffusion Modelscited by 19
  8. 2024Minority-Focused Text-to-Image Generation via Prompt Optimizationcited by 18
  9. 2022Boomerang: Local sampling on image manifolds using diffusion modelscited by 16
  10. 2022LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designercited by 15
  11. 2022Improving GANs for Long-Tailed Data through Group Spectral Regularizationcited by 14
  12. 2021Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networkscited by 13
  13. 2024Fairness and Bias Mitigation in Computer Vision: A Surveycited by 13
  14. 2023From Posterior Sampling to Meaningful Diversity in Image Restorationcited by 12
  15. 2021MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminatorscited by 11
  16. 2023Uncovering Bias in Face Generation Modelscited by 9
  17. 2023On Measuring Fairness in Generative Modelscited by 8
  18. 2024FairGen: Enhancing Fairness in Text-to-Image Diffusion Models via Self-Discovering Latent Directionscited by 8
  19. 2022Self-Conditioned Generative Adversarial Networks for Image Editingcited by 6
  20. 2023Towards Mode Balancing of Generative Models via Diversity Weightscited by 6
  21. 2022Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Samplingcited by 5
  22. 2025Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generationcited by 5
  23. 2023Uncertainty Visualization via Low-Dimensional Posterior Projectionscited by 3
  24. 2024Benchmarking the Fairness of Image Upsampling Methodscited by 3
  25. 2024PFGuard: A Generative Framework with Privacy and Fairness Safeguardscited by 3
  26. 2021Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networkscited by 2
  27. 2025Score-based Idempotent Distillation of Diffusion Modelscited by 2
  28. 2021Generative Adversarial Learning via Kernel Density Discriminationcited by 1
  29. 2021Explainability Requires Interactivitycited by 1
  30. 2026Beyond Generative Priors: Minority Sampling with JEPA-Guided Diffusioncited by 1
  31. 2022Distribution Fitting for Combating Mode Collapse in Generative Adversarial Networkscited by 0
  32. 2023Black-Box Training Data Identification in GANs via Detector Networkscited by 0