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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Diffusion-Invariant Wasserstein Distributional Classification
Authors: Sung Woo Park, Dong Wook Shu, Junseok Kwon
NeurIPS 2020 | Venue PDF | 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 EMAIL EMAIL EMAIL |
| 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. |