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].
Distributional Sliced-Wasserstein and Applications to Generative Modeling
Authors: Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct extensive experiments with large-scale datasets to demonstrate the favorable performances of the proposed distances over the previous sliced-based distances in generative modeling applications. |
| Researcher Affiliation | Collaboration | Khai Nguyen Vin AI Research, Vietnam EMAIL Nhat Ho University of Texas, Austin Vin AI Research, Vietnam EMAIL Tung Pham Vin AI Research, Vietnam EMAIL Hung Bui Vin AI Research, Vietnam EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code for the methodology described. |
| Open Datasets | Yes | We conduct extensive experiments ... on MNIST (Le Cun et al., 1998), CIFAR10 (Krizhevsky, 2009), Celeb A (Liu et al., 2015) and LSUN (Yu et al., 2015) datasets. |
| Dataset Splits | Yes | For λC in DSW (see Definition 2), it is chosen in the set {1, 10, 100, 1000} such that its Wasserstein-2 (FID score) (between 10000 random generated images and all images from corresponding validation set) is the lowest among the four values. |
| Hardware Specification | No | The paper mentions 'computational time' but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions external libraries like 'Pot python optimal transport library' and 'Geoopt: Riemannian optimization in pytorch' in its references, but it does not specify version numbers for these or any other software dependencies within the main text or experimental setup. |
| Experiment Setup | Yes | Detailed experiment settings are in Appendix G. ... We set the minibatch size be 512 on Celeb A and CIFAR, and be 4096 on LSUN. ... For λC in DSW (see Definition 2), it is chosen in the set {1, 10, 100, 1000}... We let N, the number of projections of both DSW and SW, vary in the set {1, 10, 102, 5 102, 103, 5 103, 104} for the SW, and N {1, 10, 102, 5 102, 103, 5 103} for the DSW. |