mlsanityData to 7 Oct 2026

McGan: Mean and Covariance Feature Matching GAN

arXiv 1702.08398 ↗First posted 27 Feb 2017cs.LG stat.MLcited by 21

Not on the map. Why?

Abstract (arXiv)

We introduce new families of Integral Probability Metrics (IPM) for training Generative Adversarial Networks (GAN). Our IPMs are based on matching statistics of distributions embedded in a finite dimensional feature space. Mean and covariance feature matching IPMs allow for stable training of GANs, which we will call McGan. McGan minimizes a meaningful loss between distributions.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 4; 2023: 4; 2024: 1; 2025: 1; 2026: 3.

21 papers.

Sort:

By area:

  1. 2021SynFace: Face Recognition with Synthetic Datacited by 106
  2. 2022Generative Multiplane Images: Making a 2D GAN 3D-Awarecited by 57
  3. 2021Universal Rate-Distortion-Perception Representations for Lossy Compressioncited by 46
  4. 2022StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesiscited by 38
  5. 2026Representation Fr'echet Loss for Visual Generationcited by 32
  6. 2021PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learningcited by 23
  7. 2021Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutionscited by 14
  8. 2021Manifold Learning Benefits GANscited by 14
  9. 2021Realizing GANs via a Tunable Loss Functioncited by 10
  10. 2022DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Datacited by 7
  11. 2023Addressing GAN Training Instabilities via Tunable Classification Lossescited by 5
  12. 2022A Survey on Computer Vision based Human Analysis in the COVID-19 Eracited by 3
9 more
  1. 2024GANs Conditioning Methods: A Surveycited by 3
  2. 2026AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Losscited by 3
  3. 2021Rethinking Multidimensional Discriminator Output for Generative Adversarial Networkscited by 1
  4. 2021A Unified View of cGANs with and without Classifierscited by 1
  5. 2023A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversioncited by 1
  6. 20233D GANs and Latent Space: A comprehensive surveycited by 1
  7. 2026Generating Financial Time Series by Matching Random Convolutional Featurescited by 1
  8. 2023ComGAN: Toward GANs Exploiting Multiple Samplescited by 0
  9. 2025Kernel Trace Distance: Quantum Statistical Metric between Measures through RKHS Density Operatorscited by 0