Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
Authors: Alexander Korotin, Lingxiao Li, Aude Genevay, Justin M. Solomon, Alexander Filippov, Evgeny Burnaev
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We thoroughly evaluate existing optimal transport solvers using these benchmark measures. |
| Researcher Affiliation | Collaboration | Alexander Korotin Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia a.korotin@skoltech.ru Lingxiao Li Massachusetts Institute of Technology Cambridge, Massachusetts, USA lingxiao@mit.edu Aude Genevay Massachusetts Institute of Technology Cambridge, Massachusetts, USA aude.genevay@gmail.com Justin Solomon Massachusetts Institute of Technology Cambridge, Massachusetts, USA jsolomon@mit.edu Alexander Filippov Huawei Noah s Ark Lab Moscow, Russia filippov.alexander@huawei.com Evgeny Burnaev Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia e.burnaev@skoltech.ru |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We implement our benchmark in Py Torch and provide the pre-trained transport maps for all the benchmark pairs. The code is publicly available at https://github.com/iamalexkorotin/Wasserstein2Benchmark |
| Open Datasets | Yes | Images. We use the aligned images of Celeb A64 faces dataset1 [22] to produce additional benchmark pairs. 1http://mmlab.ie.cuhk.edu.hk/projects/Celeb A.html |
| Dataset Splits | No | The paper describes generating benchmark pairs and evaluating them, but does not specify explicit train/validation/test dataset splits with percentages or counts for reproducibility in the traditional sense. |
| Hardware Specification | Yes | The experiments are conducted on 4 GTX 1080ti GPUs and require about 100 hours of computation (per GPU). |
| Software Dependencies | No | The paper mentions PyTorch but does not specify version numbers for PyTorch or any other key software libraries. |
| Experiment Setup | No | The paper mentions different neural network architectures like Dense ICNN, Conv ICNN, Res Net, and U-Net, but does not provide specific hyperparameter values or detailed system-level training configurations in the main text. |