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 [1].
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). |