Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation
Authors: Qiao Liu, Hui Xue
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results of extensive experiments on several real-world UTSDA tasks verify the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China 2MOE Key Laboratory of Computer Network and Information Integration (Southeast University), China |
| Pseudocode | Yes | Algorithm 1 Adv SKM |
| Open Source Code | Yes | Our codes 1 are published online. 1https://github.com/jarheadjoe/Adv-spec-ker-matching |
| Open Datasets | Yes | The first dataset is the Human Activity Recognition (HAR) dataset [Anguita et al., 2013] which contains accelerometer, gyroscope, and estimated body acceleration data from 30 participants. The second is the Heterogeneity Human Activity Recognition (HHAR) dataset [Stisen et al., 2015]... Next is WISDM activity recognition (WS AR) dataset [Kwapisz et al., 2011]... Finally, hand gesture accelerometer data from 8 participants is provided from the gesture recognition dataset (u Wave) [Liu et al., 2009]. |
| Dataset Splits | Yes | We split the data in each dataset into training, validation and test. The training-test were split at 80% and 20%, respectively, and the training data were further split into training-validation with the same proportions. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions implementing the framework and using the ADAM algorithm, but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The framework was trained for 30,000 iterations, which means that the best model was selected from nine models. The influence of parameter variations on accuracy is shown in Fig. 5(a)-5(b). Hyperparameters λ, L and M. |