Out-of-distribution Representation Learning for Time Series Classification
Authors: Wang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, Xing Xie
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on seven datasets with different OOD settings across gesture recognition, speech commands recognition, wearable stress and affect detection, and sensor-based human activity recognition. Qualitative and quantitative results demonstrate that DIVERSIFY significantly outperforms other baselines and effectively characterizes the latent distributions. |
| Researcher Affiliation | Collaboration | Wang Lu1 , Jindong Wang2 , Xinwei Sun3, Yiqiang Chen1, Xing Xie2 1Institute of Comput. Tech., CAS 2Microsoft Research Asia 3Fudan University |
| Pseudocode | No | The paper describes the steps of the DIVERSIFY algorithm in narrative text and figures, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code is available at https://github.com/microsoft/robustlearn. |
| Open Datasets | Yes | Extensive experiments are conducted on seven datasets with different OOD settings across gesture recognition, speech commands recognition, wearable stress and affect detection, and sensor-based human activity recognition... EMG for gestures Data Set (Lobov et al., 2018)... Speech Commands dataset (Warden, 2018)... Wearable Stress and Affect Detection (WESAD) (Schmidt et al., 2018)... DSADS (Barshan & Y uksek, 2014), USC-HAD (Zhang & Sawchuk, 2012), UCI-HAR (Anguita et al., 2012), and PAMAP (Reiss & Stricker, 2012). |
| Dataset Splits | Yes | We conduct the training-domain-validation strategy and the training data are split by 8 : 2 for training and validation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)' but does not provide a specific version number for PyTorch or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | The maximum training epoch is set to 150. The Adam optimizer with weight decay 5 10 4 is used. The learning rate for GILE is 10 4. The learning rate for the rest methods is 10 2 or 10 3. (For Speech Commands with Match Box Net3-1-64, we also try the learning rate, 10 4.) We tune hyperparameters for each method. Detailed data pre-processing, architecture, and hyperparameters are in Appendix C.5 and D. |