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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Extremal Domain Translation with Neural Optimal Transport
Authors: Milena Gazdieva, Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithm on toy examples and on the unpaired image-to-image translation task. |
| Researcher Affiliation | Collaboration | Milena Gazdieva Skolkovo Institute of Science and Technology Moscow, Russia EMAIL 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: Procedure to compute the IT map between P and Q for transport cost c(x, y) and weight w. |
| Open Source Code | Yes | The code is publicly available at https://github.com/milenagazdieva/Extremal Neural Optimal Transport |
| Open Datasets | Yes | Image datasets. We utilize the following publicly available datasets as P, Q: celebrity faces [46], aligned anime faces3, flickr-faces-HQ [36], comic faces4, Amazon handbags from LSUN dataset [68], shoes [67], textures [16] and chairs from Bonn furniture styles dataset [1]. |
| Dataset Splits | No | The paper specifies a 'Train-test split' with 90% for training and 10% for test, but does not explicitly mention a separate validation split or how hyperparameters were tuned. |
| Hardware Specification | Yes | In general, it takes from 2 (for w = 1) up to 6 (for w = 8) days on a single Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch framework' but does not specify a version number for it or other software dependencies. |
| Experiment Setup | Yes | Optimization. We employ Adam [38] optimizer with the default betas both for Tθ and fψ. The learning rate is lr = 10 4. We use the Multi Step LR scheduler which decreases lr by 2 after [(5+5 w)K, (20+5 w)K, (40+5 w)K, (70+5 w)K] iterations of fψ where w {1, 2, 4, 8} is a weight parameter. The batch size is |X| = 256 for toy Wi-Fi , |X| = 4096 for toy Accept, and |X| = 64 for image-to-image translation experiments. The number of inner Tθ iterations is k T = 10. |