Distributional Sliced-Wasserstein and Applications to Generative Modeling
Authors: Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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 v.khainb@vinai.io Nhat Ho University of Texas, Austin Vin AI Research, Vietnam minhnhat@utexas.edu Tung Pham Vin AI Research, Vietnam v.tungph4@vinai.io Hung Bui Vin AI Research, Vietnam v.hungbh1@vinai.io |
| 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. |