atlas

Research map / Image generation / GANs

Mixture Density Generative Adversarial Networks

CVPR 2019 · Image generation › GANs · approach: Stabilized GAN training · 45 citations (OpenAlex)

Check your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a simplex Gaussian mixture likelihood to encourage discriminator embedding clustersGAN training (MD-GAN) · MNTFID: 6.7 -> 6.29Helped
Use a simplex Gaussian mixture likelihood to encourage discriminator embedding clustersGAN training (MD-GAN) · FMNTFID: 18.0 -> 11.79Helped
Use a simplex Gaussian mixture likelihood to encourage discriminator embedding clustersGAN training (MD-GAN) · CFRFID: 52.9 -> 36.80Helped
Use a simplex Gaussian mixture likelihood to encourage discriminator embedding clustersGAN training (MD-GAN) · ClbFID: 26.8 -> 24.51Helped
Use a simplex Gaussian mixture likelihood to encourage discriminator embedding clustersGAN training (MD-GAN) · 2D-grid of 25 Gaussiansmodes: 24.6 -> 25Helped
Use a simplex Gaussian mixture likelihood to encourage discriminator embedding clustersGAN training (MD-GAN) · 2D-grid of 25 Gaussians% hq: 40 -> 99.36 ± 2.28Helped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy