Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Neural Optimal Transport
Authors: Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our optimal transport algorithm on toy examples and on the unpaired image-to-image translation. (a) Celeba (female) anime, outdoor church, deterministic (one-to-one, W2). (b) Handbags shoes, stochastic (one-to-many, W2,1). Figure 1: Unpaired translation with our Neural Optimal Transport (NOT) Algorithm 1. |
| Researcher Affiliation | Academia | Alexander Korotin Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia EMAIL Daniil Selikhanovych Skolkovo Institute of Science and Technology Moscow, Russia EMAIL Evgeny Burnaev Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia EMAIL |
| Pseudocode | Yes | Algorithm 1: Neural optimal transport (NOT) |
| Open Source Code | Yes | The code is written in Py Torch framework and is publicly available at https://github.com/iamalexkorotin/Neural Optimal Transport |
| Open Datasets | Yes | We use the following publicly available datasets as P, Q: aligned anime faces3, celebrity faces (Liu et al., 2015), shoes (Yu & Grauman, 2014), Amazon handbags, churches from LSUN dataset (Yu et al., 2015), outdoor images from the MIT places database (Zhou et al., 2014). |
| Dataset Splits | No | Train-test split. We pick 90% of each dataset for unpaired training. The rest 10% are considered as the test set. No explicit validation split is mentioned for the main experiments. |
| Hardware Specification | Yes | Our networks converge in 1-3 days on a Tesla V100 GPU (16 GB); wall-clock times depend on the datasets and the image sizes. Training stochastic T(x, z) is harder since we sample multiple random z per x (we use |Z| = 4). Thus, we learn stochastic maps on 4 Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch framework' and 'Adam optimizer (Kingma & Ba, 2014)' but does not specify exact version numbers for these software dependencies. |
| Experiment Setup | Yes | The learning rate is lr = 1 × 10−4. The batch size is |X| = 64. The number of inner iterations is kT = 10. When training with the weak cost (4), we sample |Zx| = 4 noise samples per each image x in batch. |