mlsanityData to 14 Oct 2026

BEGAN: Boundary Equilibrium Generative Adversarial Networks

arXiv 1703.10717 ↗First posted 31 Mar 2017cs.LG stat.MLcited by 121

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Abstract (arXiv)

We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks. This method balances the generator and discriminator during training. Additionally, it provides a new approximate convergence measure, fast and stable training and high visual quality. We also derive a way of controlling the trade-off between image diversity and visual quality. We focus on the image generation task, setting a new milestone in visual quality, even at higher resolutions. This is achieved while using a relatively simple model architecture and a standard training procedure.

Key facts. Cited by 121 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Mar 2017.

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Citing papers per year: 2018: 2; 2019: 0; 2020: 1; 2021: 47; 2022: 25; 2023: 23; 2024: 8; 2025: 12; 2026: 3.

121 papers.

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  1. 2022Generative Adversarial Networkscited by 5,627
  2. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,043
  3. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  4. 2021Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesiscited by 140
  5. 2021Image-to-Image Translation: Methods and Applicationscited by 116
  6. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 109
  7. 2021Regularizing Generative Adversarial Networks under Limited Datacited by 107
  8. 2018Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flowcited by 105
  9. 2021Deep Generative Models in Engineering Design: A Reviewcited by 105
  10. 2024Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspectivecited by 89
  11. 2021Generative Modeling with Optimal Transport Mapscited by 82
  12. 2021Towards Discovery and Attribution of Open-world GAN Generated Imagescited by 66
38 more
  1. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  2. 2022Generative Multiplane Images: Making a 2D GAN 3D-Awarecited by 57
  3. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  4. 2023Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generationcited by 52
  5. 2023Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detectioncited by 41
  6. 2021DISSECT: Disentangled Simultaneous Explanations via Concept Traversalscited by 37
  7. 2021Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)cited by 35
  8. 2021Deep Learning-based Face Super-Resolution: A Surveycited by 32
  9. 2021Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networkscited by 32
  10. 2022DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualizationcited by 32
  11. 2022TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Networkcited by 29
  12. 2023A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRUcited by 25
  13. 2023AI-Generated Content (AIGC) for Various Data Modalities: A Surveycited by 25
  14. 2023Learning Stochastic Dynamical System via Flow Map Operatorcited by 24
  15. 2021Improving GAN Equilibrium by Raising Spatial Awarenesscited by 22
  16. 2024Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learningcited by 20
  17. 2021AMP: Adversarial Motion Priors for Stylized Physics-Based Character Controlcited by 18
  18. 2021MCDAL: Maximum Classifier Discrepancy for Active Learningcited by 18
  19. 2021Structure-Preserving Image Super-Resolutioncited by 18
  20. 2022Exposing Deepfake Face Forgeries with Guided Residualscited by 17
  21. 2023AdaptNet: Policy Adaptation for Physics-Based Character Controlcited by 16
  22. 2018MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generationcited by 15
  23. 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
  24. 2021Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networkscited by 13
  25. 2021A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensingcited by 13
  26. 2022Generative Adversarial Networks for Image Super-Resolution: A Surveycited by 11
  27. 2022A Review of Intelligent Music Generation Systemscited by 11
  28. 2024Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detectioncited by 11
  29. 2022Evaluating Robustness to Dataset Shift via Parametric Robustness Setscited by 10
  30. 2023Diffusing the Optimal Topology: A Generative Optimization Approachcited by 10
  31. 2022ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfercited by 9
  32. 2025AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolationcited by 9
  33. 2021Decorating Your Own Bedroom: Locally Controlling Image Generation with Generative Adversarial Networkscited by 8
  34. 2023Balanced Training for Sparse GANscited by 8
  35. 2021Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparisoncited by 7
  36. 2021DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generationcited by 7
  37. 2022Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performancecited by 7
  38. 2022DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Datacited by 7

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