Generalized Sliced Wasserstein Distances
Authors: Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo Rohde
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we compare the numerical performance of the proposed distances on the generative modeling task of SW flows and report favorable results. |
| Researcher Affiliation | Collaboration | Soheil Kolouri1 , Kimia Nadjahi2 , Umut Sim sekli2,3, Roland Badeau2, Gustavo K. Rohde4 1: HRL Laboratories, LLC., Malibu, CA, USA, 90265 2: LTCI, Télécom Paris, Institut Polytechnique de Paris, France 3: Department of Statistics, University of Oxford, UK 4: University of Virginia, Charlottesville, VA, USA, 22904 |
| Pseudocode | No | The paper states, “The whole procedure is summarized as pseudocode in the supplementary document.”, indicating that the pseudocode is not present in the main body of the paper. |
| Open Source Code | Yes | We provide the source code to reproduce the experiments of this paper.2 2See https://github.com/kimiandj/gsw. |
| Open Datasets | Yes | To move to more realistic datasets, we considered GSW flows for the hand-written digit recognition dataset, MNIST... and Finally, we applied our methodology on a larger dataset, namely Celeb A [49]. |
| Dataset Splits | No | The paper mentions using 'the training set of MNIST' and a 'pre-trained auto-encoder' for Celeb A, but it does not provide specific details on dataset split percentages, sample counts, or a detailed methodology for creating train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments, only general statements about 'computer science applications' or 'high-dimensional settings'. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation or experiments. |
| Experiment Setup | No | The paper mentions using 'the exact same optimization scheme for all methods' and 'L = 1 projection' with a '3-layer neural network' for the defining function, but it does not provide specific hyperparameters such as learning rates, batch sizes, or detailed optimizer settings for the experimental setup. |