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].
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Authors: Khai Nguyen, Tongzheng Ren, Nhat Ho
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we compare MSW distances with previous SW variants in various applications such as gradient flows, color transfer, and deep generative modeling to demonstrate the favorable performance of the MSW1. |
| Researcher Affiliation | Academia | Khai Nguyen Department of Statistics and Data Sciences The University of Texas at Austin Austin, TX 78712 EMAIL Tongzheng Ren Department of Computer Science The University of Texas at Austin Austin, TX 78712 EMAIL Nhat Ho Department of Statistics and Data Sciences The University of Texas at Austin Austin, TX 78712 EMAIL |
| Pseudocode | Yes | Algorithm 1 Max sliced Wasserstein distance |
| Open Source Code | Yes | Code for this paper is published at https://github.com/UT-Austin-Data-Science-Group/MSW. |
| Open Datasets | Yes | We compare MSW with previous baselines including SW, Max-SW, K-SW, and Max-K-SW on benchmark datasets: CIFAR10 (image size 32x32) [29], and Celeb A [36] (image size 64x64). |
| Dataset Splits | No | The paper mentions training, but does not provide explicit training/validation/test splits. It uses 'benchmark datasets' and 'standard image datasets' which implies standard splits, but these are not explicitly stated. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions using Adam [25] as an optimizer, but does not provide specific version numbers for software dependencies like Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | In the experiments, we utilize the Euler scheme with 300 timesteps and the step size is 10^-3 to move the empirical distribution... For Max-SW, Max-K-SW, i MSW, and vi MSW, we use the learning rate parameter for projecting directions η = 0.1. ... The number of training iterations is set to 50000. We update the generator Gϕ each 5 iterations while we update the feature function Fγ every iteration. The mini-batch size m is set 128 in all datasets. The learning rate for Gϕ and Fγ is 0.0002 and the optimizer is Adam [25] with parameters (β1, β2) = (0, 0.9). We use the order p = 2 for all sliced Wasserstein variants. |