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..
Keypoint-Guided Optimal Transport with Applications in Heterogeneous Domain Adaptation
Authors: Xiang Gu, Yucheng Yang, Wei Zeng, Jian Sun, Zongben Xu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments verified the effectiveness of the KPG-RL model. Code is available at https://github.com/XJTU-XGU/KPG-RL. |
| Researcher Affiliation | Academia | Xiang Gu1, Yucheng Yang1, Wei Zeng1, Jian Sun ( )123, Zongben Xu123 1 School of Mathematics and Statistics, Xi an Jiaotong University, Xi an, China 2 Pazhou Laboratory (Huangpu), Guangzhou, China 3 Peng Cheng Laboratory, Shenzhen, China EMAIL EMAIL |
| Pseudocode | No | The paper describes iterative formulas and steps for its model but does not include a formally labeled 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | Yes | Code is available at https://github.com/XJTU-XGU/KPG-RL. |
| Open Datasets | Yes | We conduct experiments on Office-31 [53] dataset. |
| Dataset Splits | No | The paper specifies training data (transported source, labeled target, unlabeled target) and test data, but does not explicitly describe a separate validation split or how it was used. |
| Hardware Specification | No | The experiments are conducted on CPU. No specific CPU model, GPU, or other hardware details are provided. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers (e.g., Python, PyTorch versions, or specific solvers). |
| Experiment Setup | Yes | ϵ is set to 0.005. ρ is a tunable parameter and set to 0.1 in our experiments, since 0.1 is a commonly used temperature in the softmax function [47, 48]. α is simply set to 0.5 in our experiments. We train the classification model (taken as a kernel SVM). |