mlsanityData to 7 Oct 2026

Discriminator optimal transport

arXiv 1910.06832 ↗First posted 15 Oct 2019stat.ML cs.LG eess.IVcited by 25

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Abstract (arXiv)

Within a broad class of generative adversarial networks, we show that discriminator optimization process increases a lower bound of the dual cost function for the Wasserstein distance between the target distribution p and the generator distribution pG. It implies that the trained discriminator can approximate optimal transport (OT) from pG to p.Based on some experiments and a bit of OT theory, we propose a discriminator optimal transport (DOT) scheme to improve generated images. We show that it improves inception score and FID calculated by un-conditional GAN trained by CIFAR-10, STL-10 and a public pre-trained model of conditional GAN by ImageNet.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Oct 2019.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 4; 2024: 3; 2025: 0; 2026: 1.

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  1. 2021Score-based Generative Modeling in Latent Spacecited by 634
  2. 2021Likelihood Training of Schr"odinger Bridge using Forward-Backward SDEs Theorycited by 216
  3. 2022SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusioncited by 125
  4. 2021MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retrainingcited by 23
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  6. 2023Unifying GANs and Score-Based Diffusion as Generative Particle Modelscited by 21
  7. 2022FairStyle: Debiasing StyleGAN2 with Style Channel Manipulationscited by 20
  8. 2022Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?cited by 19
  9. 2023Complexity Matters: Rethinking the Latent Space for Generative Modelingcited by 19
  10. 2022Taming Hyperparameter Tuning in Continuous Normalizing Flows Using the JKO Schemecited by 17
  11. 2022Learning Probabilistic Models from Generator Latent Spaces with Hat EBMcited by 11
  12. 2021BIGRoC: Boosting Image Generation via a Robust Classifiercited by 9
13 more
  1. 2024Variational Schr"odinger Diffusion Modelscited by 9
  2. 2021Latent reweighting, an almost free improvement for GANscited by 6
  3. 2023Optimal Budgeted Rejection Sampling for Generative Modelscited by 5
  4. 2026Efficient Generative Modeling beyond Memoryless Diffusion via Adjoint Schr"odinger Bridge Matchingcited by 4
  5. 2024Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Densitycited by 3
  6. 2021Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernelscited by 2
  7. 2021Reparameterized Sampling for Generative Adversarial Networkscited by 2
  8. 2022A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applicationscited by 2
  9. 2023Structural Adversarial Objectives for Self-Supervised Representation Learningcited by 2
  10. 2024Deep Generative Quantile Bayescited by 1
  11. 2021Sliced L2 Distance for Colour Gradingcited by 0
  12. 2021A Generic Approach for Enhancing GANs by Regularized Latent Optimizationcited by 0
  13. 2022Multisymplectic Formulation of Deep Learning Using Mean--Field Type Control and Nonlinear Stability of Training Algorithmcited by 0