Scale and Direction Guided GAN for Inertial Sensor Signal Enhancement
Authors: Yifeng Wang, Yi Zhao
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that both the unsupervised SGGAN and the weakly-supervised DG-GAN significantly outperform all comparison methods, including fully-supervised approaches. |
| Researcher Affiliation | Academia | Yifeng Wang , Yi Zhao School of Science, Harbin Institute of Technology, Shenzhen wangyifeng@stu.hit.edu.cn, zhao.yi@hit.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that its source code is open-source or provide a link to a code repository for its methodology. |
| Open Datasets | No | The paper describes collecting its own dataset using 15 smartphones: "We take 15 smartphones with the built-in IMUS to collect the inertial dataset, of which one type of smartphone is employed for collecting the training set, while the data of all the other phones are used for testing." However, it does not provide any access information (e.g., URL, DOI, specific citation to a public repository) for this dataset. |
| Dataset Splits | Yes | We take 15 smartphones with the built-in IMUS to collect the inertial dataset, of which one type of smartphone is employed for collecting the training set, while the data of all the other phones are used for testing. |
| Hardware Specification | Yes | All experiments are implemented by Pytorch 1.10.1 with an Nvidia RTX 2080TI GPU and Intel(R) Xeon(R) W-2133 CPU. |
| Software Dependencies | Yes | All experiments are implemented by Pytorch 1.10.1 with an Nvidia RTX 2080TI GPU and Intel(R) Xeon(R) W-2133 CPU. |
| Experiment Setup | No | The paper describes the architecture of the proposed GAN models and the loss functions used. However, it does not explicitly provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or other detailed system-level training configurations needed for reproduction. |