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Generative Multi-Adversarial Networks

arXiv 1611.01673 ↗First posted 5 Nov 2016cs.LG cs.MA cs.NEcited by 60

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Abstract (arXiv)

Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the Generative Multi-Adversarial Network (GMAN), a framework that extends GANs to multiple discriminators. In previous work, the successful training of GANs requires modifying the minimax objective to accelerate training early on. In contrast, GMAN can be reliably trained with the original, untampered objective. We explore a number of design perspectives with the discriminator role ranging from formidable adversary to forgiving teacher. Image generation tasks comparing the proposed framework to standard GANs demonstrate GMAN produces higher quality samples in a fraction of the iterations when measured by a pairwise GAM-type metric.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2016.

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Citing papers per year: 2021: 29; 2022: 13; 2023: 8; 2024: 5; 2025: 2; 2026: 3.

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  1. 2021StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesiscited by 477
  2. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 221
  3. 2021Projected GANs Converge Fastercited by 204
  4. 2021Ensembling Off-the-shelf Models for GAN Trainingcited by 84
  5. 2022HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancementcited by 49
  6. 2021A Survey on Ensemble Learning under the Era of Deep Learningcited by 47
  7. 2021HumanGAN: A Generative Model of Humans Imagescited by 47
  8. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  9. 2022VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relightingcited by 37
  10. 2022Fair Generative Models via Transfer Learningcited by 27
  11. 2021MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retrainingcited by 23
  12. 2021Few-shot Image Generation with Mixup-based Distance Learningcited by 16
38 more
  1. 2024NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Trainingcited by 16
  2. 2021Generative Zero-shot Network Quantizationcited by 15
  3. 2021A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Controlcited by 12
  4. 2021MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminatorscited by 11
  5. 2023Enhanced Multimodal Representation Learning with Cross-modal KDcited by 11
  6. 2021Self-Supervised GANs with Label Augmentationcited by 9
  7. 2021Robust pedestrian detection in thermal imagery using synthesized imagescited by 7
  8. 2021Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Networkcited by 7
  9. 2021SemIE: Semantically-aware Image Extrapolationcited by 7
  10. 2023Digital staining in optical microscopy using deep learning -- a reviewcited by 7
  11. 2021DO-GAN: A Double Oracle Framework for Generative Adversarial Networkscited by 6
  12. 2021Game of GANs: Game-Theoretical Models for Generative Adversarial Networkscited by 6
  13. 2022Exposure Correction Model to Enhance Image Qualitycited by 6
  14. 2024Multi-objective Deep Learning: Taxonomy and Survey of the State of the Artcited by 6
  15. 2021DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Trainingcited by 4
  16. 2021Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Surveycited by 4
  17. 2022Facial Expression Translation using Landmark Guided GANscited by 4
  18. 2026Epistemic Generative Adversarial Networkscited by 4
  19. 2021Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Studycited by 3
  20. 2021Overcoming Mode Collapse with Adaptive Multi Adversarial Trainingcited by 3
  21. 2023Stabilizing GANs' Training with Brownian Motion Controllercited by 3
  22. 2026Unsupervised Decomposition and Recombination with Discriminator-Driven Diffusion Modelscited by 3
  23. 2021Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescencecited by 2
  24. 2022Single-level Adversarial Data Synthesis based on Neural Tangent Kernelscited by 2
  25. 2022BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Imagescited by 2
  26. 2022MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distributioncited by 2
  27. 2022PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GANcited by 2
  28. 2024Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical losscited by 2
  29. 2021IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapsecited by 1
  30. 2021Exploring Dropout Discriminator for Domain Adaptationcited by 1
  31. 2021Unsupervised Image Generation with Infinite Generative Adversarial Networkscited by 1
  32. 2021Towards convergence to Nash equilibria in two-team zero-sum gamescited by 1
  33. 2022Attacks and Defenses for Free-Riders in Multi-Discriminator GANcited by 1
  34. 2023MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networkscited by 1
  35. 2024Geometric Generative Models based on Morphological Equivariant PDEs and GANscited by 1
  36. 2024Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Datacited by 1
  37. 2025Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergencecited by 1
  38. 2021HGAN: Hybrid Generative Adversarial Networkcited by 0

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