Light Unbalanced Optimal Transport
Authors: Milena Gazdieva, Arip Asadulaev, Evgeny Burnaev, Aleksandr Korotin
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide the generalization bounds for our solver (M4.4) and experimentally test it on several tasks (M5.1, M5.2). ... In this section, we test our U-Light OT solver on several setups from the related works. |
| Researcher Affiliation | Academia | Milena Gazdieva Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia milena.gazdieva@skoltech.ru Arip Asadulaev ITMO University Artificial Intelligence Research Institute Moscow, Russia asadulaev@airi.net Evgeny Burnaev Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia e.burnaev@skoltech.ru Alexander Korotin Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia a.korotin@skoltech.ru |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code is publicly available at https://github.com/milenagazdieva/Light Unbalanced Optimal Transport |
| Open Datasets | Yes | We follow the setup of [41, Section 5.4] and use pre-trained ALAE autoencoder [55] for 1024 1024 FFHQ dataset [34] of human faces. |
| Dataset Splits | No | The paper mentions 'Number of train FFHQ images for each subset' in Table 2, but does not explicitly state specific train/validation/test dataset splits (e.g., percentages or sample counts for each split) for its experiments. |
| Hardware Specification | Yes | Each experiment requires several minutes of training on CPU with 4 cores. |
| Software Dependencies | No | The code is written using Py Torch framework and is publicly available at... We use the Adam optimizer... To obtain an optimal transport plan π (x, y) discrete OT solvers from the POT [21] package were used. |
| Experiment Setup | Yes | We use K = L = 5, ε = 0.05, lr = 3e 4 and batchsize 128. We do 2 104 gradient steps... for our solver, we use weighted DKL divergence with parameters τ specified in Appendix C, and set K = L = 10, ε = 0.05, lr = 1, and batch size to 128. We do 5 103 gradient steps using Adam optimizer [35] and Multi Step LR scheduler with parameter γ = 0.1 and milestones= [500, 1000]. |