Data to 7 Oct 2026

Energy-based Generative Adversarial Network

arXiv 1609.03126 ↗First posted 11 Sep 2016cs.LG stat.MLcited by 119

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Abstract (arXiv)

We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discriminator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the discriminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images.

Key facts. Cited by 119 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2016.

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Citing papers per year: 2018: 2; 2019: 0; 2020: 2; 2021: 37; 2022: 23; 2023: 24; 2024: 23; 2025: 6; 2026: 2.

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2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
2022Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptionscited by 215
2024Diffusion Model-Based Image Editing: A Surveycited by 212
2021Projected GANs Converge Fastercited by 204
2022Convergence for score-based generative modeling with polynomial complexitycited by 176
2021Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesiscited by 140
2021Image-to-Image Translation: Methods and Applicationscited by 116
2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
2021Regularizing Generative Adversarial Networks under Limited Datacited by 105
2018Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flowcited by 103
2024Towards Generalizable Tumor Synthesiscited by 65

38 more

2023Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligencecited by 52
2021Energy-Based Open-World Uncertainty Modeling for Confidence Calibrationcited by 46
2024Diffusion Policies creating a Trust Region for Offline Reinforcement Learningcited by 41
2020Energy-Based Imitation Learningcited by 39
2024Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalizationcited by 39
2022StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesiscited by 38
2021DISSECT: Disentangled Simultaneous Explanations via Concept Traversalscited by 37
2021Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastingscited by 37
2018Sample-Efficient Imitation Learning via Generative Adversarial Netscited by 36
2021Autoencoding Under Normalization Constraintscited by 26
2021MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retrainingcited by 23
2022Topology Optimization via Machine Learning and Deep Learning: A Reviewcited by 22
2021ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose Estimationcited by 20
2022End-to-End Stochastic Optimization with Energy-Based Modelcited by 19
2023Binary Latent Diffusioncited by 18
2024ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidancecited by 18
2023EGC: Image Generation and Classification via a Diffusion Energy-Based Modelcited by 16
2024Energy-based Automated Model Evaluationcited by 16
2020Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learningcited by 15
2021Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Reviewcited by 15
2023Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyondcited by 14
2021Rates of convergence for density estimation with generative adversarial networkscited by 13
2021Adversarial sampling of unknown and high-dimensional conditional distributionscited by 13
2022Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utilitycited by 12
2021Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluationcited by 11
2022Generative Adversarial Networks for Image Super-Resolution: A Surveycited by 11
2023Single-Model Attribution of Generative Models Through Final-Layer Inversioncited by 10
2023PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendationcited by 9
2021Generalization Error of GAN from the Discriminator's Perspectivecited by 8
2025Image Editing with Diffusion Models: A Surveycited by 8
2021DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generationcited by 7
2022A Mathematical Framework for Learning Probability Distributionscited by 7
2023Energy-Inspired Self-Supervised Pretraining for Vision Modelscited by 7
2023Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionalscited by 7
2024Improving Adversarial Energy-Based Model via Diffusion Processcited by 7
2024Controllable Text Generation in the Instruction-Tuning Eracited by 7
2021Training Generative Adversarial Networks in One Stagecited by 6
2021Inferential Wasserstein Generative Adversarial Networkscited by 6

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