Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation
Authors: Yuguang Yan, Wen Li, Hanrui Wu, Huaqing Min, Mingkui Tan, Qingyao Wu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1School of Software Engineering, South China University of Technology, China 2Computer Vision Laboratory, ETH Zurich, Switzerland |
| Pseudocode | Yes | Algorithm 1 summarizes the main steps of the proposed SGW algorithm. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The synthetic data are generated from Wine dataset 1, which includes 178 samples with three classes. 1https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ Object Recognition: We use Office and Caltech-256 datasets for the object recognition task. Office [Saenko et al., 2010]... Caltech-256 (C) [Griffin et al., 2007]... Text Classification: The text classification task is conducted on the Reuters multilingual dataset [Amini et al., 2009] |
| Dataset Splits | No | The paper describes how training and test data are used but does not explicitly define a separate validation set or split for model tuning. |
| Hardware Specification | Yes | The experiments are performed on a workstation with Xeon 3.40 GHz CPU and 16 GB of RAM. |
| Software Dependencies | No | The paper mentions using a classifier (SVM), but does not provide specific version numbers for any software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | For simplicity and fair comparison, we set the trade-off parameter of SVM to C = 1 for all the methods and tasks. The parameters of SGW are empirically set to ϵ = 0.01, λ = 1 and γ = 1, and the sensitivity study is provided in Section 5.6. |