Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Authors: Khai Nguyen, Dang Nguyen, Nhat Ho
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the effectiveness of our proposal, we evaluate our methods on the point-cloud reconstruction task and its two downstream tasks including transfer learning and point-cloud generation. The quantitative results are summarized in Table 1. |
| Researcher Affiliation | Collaboration | 1Department of Statistics and Data Sciences, University of Texas at Austin, USA 2Vin AI Research. |
| Pseudocode | Yes | Algorithm 1 Sampling from v MF distribution |
| Open Source Code | Yes | Code for the paper is published at https://github. com/hsgser/Self-Amortized-DSW. |
| Open Datasets | Yes | Our autoencoder is trained on the Shape Net Core-55 dataset (Chang et al., 2015) with a batch size of 128 and a point-cloud size of 2048. We train it for 300 epochs using an SGD optimizer with an initial learning rate of 1e-3, a momentum of 0.9, and a weight decay of 5e-4. |
| Dataset Splits | Yes | the chair category of Shape Net is divided into train/valid/test sets in an 85/5/10 ratio. |
| Hardware Specification | Yes | All experiments are run on NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions using an 'Adam optimizer (Kingma & Ba, 2014)' but does not provide specific version numbers for software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Our autoencoder is trained on the Shape Net Core-55 dataset (Chang et al., 2015) with a batch size of 128 and a point-cloud size of 2048. We train it for 300 epochs using an SGD optimizer with an initial learning rate of 1e-3, a momentum of 0.9, and a weight decay of 5e-4. |