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..
SageMix: Saliency-Guided Mixup for Point Clouds
Authors: Sanghyeok Lee, Minkyu Jeon, Injae Kim, Yunyang Xiong, Hyunwoo J. Kim
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the proposed method consistently outperforms existing Mixup methods in various benchmark point cloud datasets. 4 Experiments In this section, we demonstrate the effectiveness of our proposed method Sage Mix with various benchmark datasets. |
| Researcher Affiliation | Collaboration | Sanghyeok Lee Korea University EMAIL Minkyu Jeon Korea University EMAIL Injae Kim Korea University EMAIL Yunyang Xiong Meta Reality Labs EMAIL Hyunwoo J. Kim Korea University EMAIL |
| Pseudocode | Yes | The overall pipeline is illustrated in Figure 2 and pseudocode is provided in Algorithm 1 . Algorithm 1 A saliency-guided Mixup for point clouds |
| Open Source Code | Yes | Code is available at https://github.com/mlvlab/Sage Mix. |
| Open Datasets | Yes | We use two benchmark dataset: 3D Warehouse dataset (MN40) [18] and Scan Object NN [19]. |
| Dataset Splits | Yes | MN40 is a synthetic dataset containing 9,843 CAD models for training and 2,468 CAD models for evaluation. Scan Object NN... is a real-world dataset that is split into 80% for training and 20% for evaluation. |
| Hardware Specification | No | The paper states 'National Supercomputing Center with supercomputing resources including technical support (KSC-2022-CRE-0261)' in the acknowledgments, but does not provide specific hardware details like GPU or CPU models in the main text. |
| Software Dependencies | No | The paper does not explicitly provide specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | No | Implementation details are provided in Appendix A. (However, Appendix A itself is not provided in the input text, thus specific values are not present in the given context). |