Deep Diffusion-Invariant Wasserstein Distributional Classification
Authors: Sung Woo Park, Dong Wook Shu, Junseok Kwon
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For empirical validation, we applied our method to two classification problems: 3D point cloud classification using the Model Net10 (28) dataset and image classification using the CIFAR10 dataset (12), where the data suffer from various perturbations. ... Table 1: 2D image classification accuracy (in %) on the CIFAR10 dataset with perturbations. ... Table 2: 3D point cloud classification accuracy (in %) for the Model Net10 dataset with perturbations. |
| Researcher Affiliation | Academia | Sung Woo Park Dong Wook Shu Junseok Kwon School of Computer Science and Engineering Chung-Ang University, Seoul, Korea pswkiki@gmail.com seowok@naver.com jskwon@cau.ac.kr |
| Pseudocode | Yes | Algorithm 1 Deep WDC |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | For empirical validation, we applied our method to two classification problems: 3D point cloud classification using the Model Net10 (28) dataset and image classification using the CIFAR10 dataset (12) |
| Dataset Splits | No | The paper mentions using CIFAR10 and ModelNet10 datasets but does not explicitly provide specific details about the training, validation, or test splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All experiments were executed using a single RTX 2080 TI GPU. |
| Software Dependencies | Yes | Our method was implemented using Pytorch1.4.0 and Python3.6. |
| Experiment Setup | Yes | We used the ADAM optimizer with a learning rate of 10 5 for the network g and a learning rate of 10 3 for the network f, as well as for the baseline networks. |