Entropic Neural Optimal Transport via Diffusion Processes
Authors: Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry P. Vetrov, Evgeny Burnaev
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show the performance of the method on several large-scale EOT tasks. The code for the ENOT solver can be found at https://github.com/ngushchin/Entropic Neural Optimal Transport. In this section, we qualitatively and quantitatively illustrate the performance of our algorithm in several entropic OT tasks. |
| Researcher Affiliation | Collaboration | Nikita Gushchin Skoltech Moscow, Russia n.gushchin@skoltech.ru Alexander Kolesov Skoltech Moscow, Russia a.kolesov@skoltech.ru Alexander Korotin Skoltech , AIRI Moscow, Russia a.korotin@skoltech.ru Dmitry Vetrov HSE University , AIRI Moscow, Russia vetrovd@yandex.ru Evgeny Burnaev Skoltech , AIRI Moscow, Russia e.burnaev@skoltech.ru |
| Pseudocode | Yes | Algorithm 1: Entropic Neural OT (ENOT) Input: samples from distributions P0, P1; Wiener prior noise variance ϵ 0; drift network fθ : RD [0, 1] RD; beta network βϕ : RD R; number of steps N for Eul-Mar (App C); number of inner iterations Kf. Output: drift f θ of Tf θ solving DSB (11). Algorithm 2: Euler-Maruyama algorithm |
| Open Source Code | Yes | The code for the ENOT solver can be found at https://github.com/ngushchin/Entropic Neural Optimal Transport. The code is written in Py Torch and is publicly available at https://github.com/ngushchin/Entropic Neural Optimal Transport |
| Open Datasets | Yes | We consider the entropic OT problem between colorized MNIST digits of classes '2' (P0) and '3' (P1). We consider the problem of unpaired image super-resolution for the 64 64 aligned faces of Celeb A dataset [35]. |
| Dataset Splits | No | We do the unpaired train-test split as follows: we split the dataset into 3 parts: 90k (train A1), 90k (train B1), 20k (test C1) samples. For training in the unpaired setup, we use parts A0 (degraded faces, P0) and B1 (clean faces, P1). The paper specifies train and test splits, but no explicit validation split. |
| Hardware Specification | Yes | In the most challenging task (M5.4), ENOT converges in one week on 2 A100 GPUs. |
| Software Dependencies | No | The code is written in Py Torch. The paper mentions PyTorch but does not provide a specific version number for it or any other software dependency. |
| Experiment Setup | Yes | In all the cases, we use N = 10 discretization steps for solving SDE by Euler-Maruyama Algorithm 2, Adam with lr = 10 4, batch size 512. We train the model for 20000 total iterations of βϕ, and on each of them, we do Kf = 10 updates for the SDE drift function fθ. We use Adam with lr = 10 4, batch size 64 and 10:1 update ratio for fθ/βϕ. |