Quasi-Monte Carlo for 3D Sliced Wasserstein
Authors: Khai Nguyen, Nicola Bariletto, Nhat Ho
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments on various 3D tasks, such as point-cloud comparison, point-cloud interpolation, image style transfer, and training deep point-cloud autoencoders, to demonstrate the favorable performance of the proposed QSW and RQSW variants1. |
| Researcher Affiliation | Academia | Khai Nguyen, Nicola Bariletto & Nhat Ho Department of Statistics and Data Sciences The University of Texas at Austin Austin, TX 78712, USA {khainb,nicola.bariletto,minhnhat}@utexas.edu |
| Pseudocode | Yes | Algorithm 1 Monte Carlo estimation of the Sliced Wasserstein distance. Algorithm 2 Quasi-Monte Carlo approximation of the sliced Wasserstein distance. Algorithm 3 Randomized Quasi-Monte Carlo estimation of the Sliced Wasserstein distance with scrambling. Algorithm 4 The Randomized Quasi-Monte Carlo estimation of sliced Wasserstein distance with random rotation. |
| Open Source Code | Yes | 1Code for the paper is published at https://github.com/khainb/Quasi-SW. |
| Open Datasets | Yes | We select randomly four point-clouds (1, 2, 3, and 4 with 3 dimensions, 2048 points) from Shape Net Core-55 dataset (Chang et al., 2015) as shown in Figure 1. |
| Dataset Splits | No | The paper discusses training and testing, and mentions L=100 as the number of projections, but does not provide specific details on how the dataset was split into train/validation/test sets by percentages or counts. |
| Hardware Specification | Yes | We use a single NVIDIA V100 GPU to conduct experiments on training deep point-cloud autoencoder. Other applications are done on a desktop with an Intel core I5 CPU chip. |
| Software Dependencies | No | The paper mentions "POT library, Flamary et al. (2021)" but does not specify its version number. It also mentions "Point-Net Qi et al. (2017) architecture" but no software versions are provided for this or other dependencies. |
| Experiment Setup | Yes | We aim to optimize the following objective minϕ,γ EX µ(X)[SWp(PX, Pgγ(fϕ(X)))], where µ(X) is our data distribution, fϕ and gψ are a deep encoder and a deep decoder with Point-Net Qi et al. (2017) architecture. To optimize the objective, we use conventional MC estimation, QSW, and RQSW to approximate the gradient ϕ and ψ. We then utilize the standard SGD optimizer to train the autoencoder (with an embedding size of 256) for 400 epochs with a learning rate of 1e-3, a batch size of 128, a momentum of 0.9, and a weight decay of 5e-4. |