mlsanityData to 7 Oct 2026

MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators

arXiv 2107.07260 ↗First posted 15 Jul 2021cs.LGcited by 11

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Abstract (arXiv)

We propose a framework of generative adversarial networks with multiple discriminators, which collaborate to represent a real dataset more effectively. Our approach facilitates learning a generator consistent with the underlying data distribution based on real images and thus mitigates the chronic mode collapse problem. From the inspiration of multiple choice learning, we guide each discriminator to have expertise in a subset of the entire data and allow the generator to find reasonable correspondences between the latent and real data spaces automatically without extra supervision for training examples. Despite the use of multiple discriminators, the backbone networks are shared across the discriminators and the increase in training cost is marginal. We demonstrate the effectiveness of our algorithm using multiple evaluation metrics in the standard datasets for diverse tasks.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 40 of its 56 references that are in the map. First posted on arXiv 15 Jul 2021.

Builds on

40 of 56 references.

References · 40

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  11. 2018Assessing Generative Models via Precision and Recallcited by 401
  12. 2016Adversarial Feature Learningcited by 350
28 more
  1. 2018Diverse Image-to-Image Translation via Disentangled Representationscited by 296
  2. 2019DRIT++: Diverse Image-to-Image Translation via Disentangled Representationscited by 244
  3. 2017Geometric GANcited by 232
  4. 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
  5. 2017Photographic Image Synthesis with Cascaded Refinement Networkscited by 223
  6. 2016Adversarially Learned Inferencecited by 216
  7. 2017GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibriumcited by 208
  8. 2017VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learningcited by 199
  9. 2016Semantic Image Inpainting with Deep Generative Modelscited by 156
  10. 2019Mode Seeking Generative Adversarial Networks for Diverse Image Synthesiscited by 138
  11. 2017PacGAN: The power of two samples in generative adversarial networkscited by 104
  12. 2016Stochastic Multiple Choice Learning for Training Diverse Deep Ensemblescited by 77
  13. 2019Diversity-Sensitive Conditional Generative Adversarial Networkscited by 77
  14. 2018On GANs and GMMscited by 62
  15. 2016Generative Multi-Adversarial Networkscited by 60
  16. 2018Diverse Image Synthesis from Semantic Layouts via Conditional IMLEcited by 60
  17. 2017Multi-Agent Diverse Generative Adversarial Networkscited by 59
  18. 2017Dual Discriminator Generative Adversarial Netscited by 55
  19. 2020Diverse Image Generation via Self-Conditioned GANscited by 54
  20. 2020Inclusive GAN: Improving Data and Minority Coverage in Generative Modelscited by 44
  21. 2017AdaGAN: Boosting Generative Modelscited by 34
  22. 2017Confident Multiple Choice Learningcited by 24
  23. 2019Multi-objective training of Generative Adversarial Networks with multiple discriminatorscited by 24
  24. 2017Stabilizing GAN Training with Multiple Random Projectionscited by 15
  25. 2018MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generationcited by 15
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  27. 2020DiverseNet: When One Right Answer is not Enoughcited by 11
  28. 2019Normalized Diversificationcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 2; 2026: 2.

11 papers.

  1. 2024NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Trainingcited by 16
  2. 2023The Representation Jensen-Shannon Divergencecited by 9
  3. 2023Generating Personas for Games with Multimodal Adversarial Imitation Learningcited by 3
  4. 2024Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical losscited by 2
  5. 2025Text-to-3D Generation using Jensen-Shannon Score Distillationcited by 2
  6. 2024Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Datacited by 1
  7. 2025Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergencecited by 1
  8. 2023PlantPlotGAN: A Physics-Informed Generative Adversarial Network for Plant Disease Predictioncited by 0
  9. 2024The Bid Picture: Auction-Inspired Multi-player Generative Adversarial Networks Trainingcited by 0
  10. 2026Approximating f-Divergences with Rank Statisticscited by 0
  11. 2026StatD2GAN: When Calibration Masks Generator Quality in Held-Out Evaluation of Synthetic Weather Sequencescited by 0