Data to 7 Oct 2026

Generative Moment Matching Networks

arXiv 1502.02761 ↗First posted 10 Feb 2015cs.LG cs.AI stat.MLcited by 252

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Cited by 21 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We consider the problem of learning deep generative models from data. We formulate a method that generates an independent sample via a single feedforward pass through a multilayer perceptron, as in the recently proposed generative adversarial networks (Goodfellow et al., 2014). Training a generative adversarial network, however, requires careful optimization of a difficult minimax program. Instead, we utilize a technique from statistical hypothesis testing known as maximum mean discrepancy (MMD), which leads to a simple objective that can be interpreted as matching all orders of statistics between a dataset and samples from the model, and can be trained by backpropagation. We further boost the performance of this approach by combining our generative network with an auto-encoder network, using MMD to learn to generate codes that can then be decoded to produce samples. We show that the combination of these techniques yields excellent generative models compared to baseline approaches as measured on MNIST and the Toronto Face Database.

Key facts. Cited by 252 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2015.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 57; 2022: 41; 2023: 38; 2024: 34; 2025: 40; 2026: 40.

252 papers.

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2023One-step Diffusion with Distribution Matching Distillationcited by 978
2021Dataset Condensation with Distribution Matchingcited by 362
2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
2019A Divergence Minimization Perspective on Imitation Learning Methodscited by 188
2021Neural SDEs as Infinite-Dimensional GANscited by 159
2023Improved Distribution Matching for Dataset Condensationcited by 124
2025Inductive Moment Matchingcited by 107
2021Generalized Decision Transformer for Offline Hindsight Information Matchingcited by 105
2026Generative Modeling via Driftingcited by 98
2023DataDAM: Efficient Dataset Distillation with Attention Matchingcited by 83
2021Efficient and Accurate Gradients for Neural SDEscited by 68
2022Synthetic Data -- what, why and how?cited by 66

38 more

2022The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matricescited by 62
2023A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Futurecited by 59
2024Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generationcited by 56
2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
2021Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentationcited by 53
2021Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projectionscited by 52
2023Vector Quantized Time Series Generation with a Bidirectional Prior Modelcited by 49
2021Improving Conditional Coverage via Orthogonal Quantile Regressioncited by 48
2024Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connectionscited by 47
2021SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentationcited by 41
2022Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrapcited by 41
2020Energy-Based Imitation Learningcited by 39
2021Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectorscited by 37
2021KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Supportcited by 37
2021Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nystr"om Method, and Use of Kernels in Machine Learning: Tutorial and Surveycited by 36
2023Generative Sliced MMD Flows with Riesz Kernelscited by 36
2023Non-adversarial training of Neural SDEs with signature kernel scorescited by 36
2021An error analysis of generative adversarial networks for learning distributionscited by 35
2021Probabilistic Forecasting with Generative Networks via Scoring Rule Minimizationcited by 35
2023DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classificationcited by 32
2026Representation Fr'echet Loss for Visual Generationcited by 32
2024DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systemscited by 29
2022Pre-trained Perceptual Features Improve Differentially Private Image Generationcited by 28
2023Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentationcited by 28
2022Generative machine learning methods for multivariate ensemble post-processingcited by 26
2023Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentationcited by 26
2025Distributional Diffusion Models with Scoring Rulescited by 25
2022Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfecited by 22
2021Generative adversarial networks in time series: A survey and taxonomycited by 20
2021On the capacity of deep generative networks for approximating distributionscited by 19
2022Enhancing Sequential Recommendation with Graph Contrastive Learningcited by 18
2025Proper scoring rules for estimation and forecast evaluationcited by 18
2025Align-Then-stEer: Adapting the Vision-Language Action Models through Unified Latent Guidancecited by 18
2026One-Step Generative Modeling via Wasserstein Gradient Flowscited by 18
2021Implicit Generative Copulascited by 17
2021Composite Goodness-of-fit Tests with Kernelscited by 16
2021Neural Tangent Kernel Maximum Mean Discrepancycited by 15
2022AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalizationcited by 15

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