Sliced Gromov-Wasserstein
Authors: Vayer Titouan, Rémi Flamary, Nicolas Courty, Romain Tavenard, Laetitia Chapel
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental results The goal of this section is to validate SGW and its rotational invariant on both quantitative (execution time) and qualitative sides. |
| Researcher Affiliation | Academia | Titouan Vayer Univ. Bretagne-Sud, CNRS, IRISA F-56000 Vannes titouan.vayer@irisa.fr Remi Flamary Univ. Cˆote d Azur, OCA, Lagrange F-06000 Nice remi.flamary@unice.fr Romain Tavenard Univ. Rennes, CNRS, LETG F-35000 Rennes romain.tavenard@univ-rennes2.fr Laetitia Chapel Univ. Bretagne-Sud, CNRS, IRISA F-56000 Vannes laetitia.chapel@irisa.fr Nicolas Courty Univ. Bretagne-Sud, CNRS, IRISA F-56000 Vannes nicolas.courty@irisa.fr |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using and implementing various tools (POT, Numpy, Pytorch) but does not provide a direct link or explicit statement for the release of their own source code for the Sliced Gromov-Wasserstein (SGW) method. |
| Open Datasets | Yes | As a first example, we use the spiral dataset from sklearn toolbox and compute GW, SGW and RISGW on n = 100 samples with L = 20 sampled lines for different rotations of the target distribution. |
| Dataset Splits | No | The paper mentions dataset sizes (e.g., n=100 samples, 2D random measures of n {1e2, ..., 1e6} points) but does not specify how these samples are split into training, validation, or test sets. |
| Hardware Specification | Yes | All the experiments were conducted on a standard computer equipped with a NVIDIA Titan X GPU. |
| Software Dependencies | No | The paper mentions software like "Python Optimal Transport (POT) toolbox", "Pytorch", "Pymanopt", and "autograd" but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | For SGW, the number of projections L is taken from {50, 200}. We use the Python Optimal Transport (POT) toolbox [46] to compute GW distance on CPU. For entropic-GW we use the Pytorch GPU implementation from [9] that uses the log-stabilized Sinkhorn algorithm [47] with a regularization parameter ε = 100. The Adam optimizer is used, with a learning rate of 2.10 4 and β1 = 0.5, β2 = 0.99. |