Subspace Identification for Multi-Source Domain Adaptation
Authors: Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun, Zhifeng Hao, Kun Zhang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our SIG model outperforms existing MSDA techniques on various benchmark datasets, highlighting its effectiveness in real-world applications. |
| Researcher Affiliation | Academia | 1 Carnegie Mellon University 2 School of Computer Science, Guangdong University of Technology 3 Mohamed bin Zayed University of Artificial Intelligence 4 Shantou University |
| Pseudocode | No | The paper describes its models and framework using text and diagrams (Figure 3), but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Datasets: We consider four benchmarks: Office-Home, PACS, Image CLEF, and Domain Net. |
| Dataset Splits | No | We further split the simulation dataset into the training set, validation set, and test set. While a validation set is mentioned for simulation data, specific percentages or sample counts for the splits are not provided for any dataset, nor are citations to predefined validation splits for the benchmark datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper states: "The implementation details are provided in the Appendix C." However, Appendix C is not included in the provided text, meaning the specific experimental setup details (like hyperparameters or training settings) are not in the main text. |