Distance Metric Facilitated Transportation between Heterogeneous Domains
Authors: Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan, Peng He
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic dataset validate the importance of the metric facilitated consideration, while results on real-world tasks show the superiority of the MAPHERE approach. |
| Researcher Affiliation | Collaboration | Han-Jia Ye1, Xiang-Rong Sheng1, De-Chuan Zhan1, Peng He2 1 National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2 Tencent, China |
| Pseudocode | No | The paper describes the optimization process in text and equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code, such as a repository link, an explicit code release statement, or code in supplementary materials. |
| Open Datasets | Yes | We first test the performance of MAPHERE approach on homogeneous domain adaptation task on the Office-Caltech dataset. |
| Dataset Splits | Yes | We use the same protocol (including the splits) as [Perrot and Habrard, 2015]. For each task, we repeat the investigations 20 trials, and in each trial, there are 8 labeled source examples (20 if the source is Amazon) and 3 labeled target examples are selected. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments, such as CPU or GPU models, or memory amounts. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | In the implementation, L0 is an identity matrix in the homogeneous case; while in the heterogeneous scenario, L0 comes from a least square problem mapping the source domain instances to the center of corresponding target domain classes. Two types of pairs are sampled based on Euclidean distance nearest neighbors of instances (5NN and 3NN are used to construct source and target domain same class similar pairs, while 1NN are used to generate both domains impostors). λ1 = 1 and λ2 = 10 are default parameters. |