Research map / Learning theory and algorithms / Optimal transport
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
Read the paperCheck 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 changed | Where | Measured | Effect |
|---|---|---|---|
| Use the learned scaling factor for young-to-aged CelebA transport | Unbalanced OT using GANs · CelebA Young-to-Aged | young faces with high scaling factor by gender: 9.6% (9,913/103,287) female; 18.5% (8,029/53,447) male | Mixed |
Closest papers by meaning
- Analyzing and Improving Optimal-Transport-based Adversarial Networks (ICLR 2024 · similarity 0.91)
- Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport (NeurIPS 2023 · similarity 0.91)
- Improving GANs Using Optimal Transport (ICLR 2018 · similarity 0.89)
- Parallel Optimal Transport GAN (CVPR 2019 · similarity 0.89)
- MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics (NeurIPS 2020 · similarity 0.88)
- Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation (ICLR 2024 · similarity 0.86)
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.