Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels
Authors: Zhen Liu, ma peitian, Dongliang Chen, Wenbin Pei, Qianli Ma
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple benchmark time series datasets demonstrate the superiority of the proposed Scale-teaching paradigm over state-of-the-art methods in terms of effectiveness and robustness. |
| Researcher Affiliation | Academia | Zhen Liu South China University of Technology Guangzhou, China EMAIL Peitian Ma South China University of Technology Guangzhou, China EMAIL Dongliang Chen South China University of Technology Guangzhou, China EMAIL Wenbin Pei Dalian University of Technology Dalian, China EMAIL Qianli Ma South China University of Technology Guangzhou, China EMAIL |
| Pseudocode | Yes | Please refer to Algorithm 1 in the Appendix for the specific pseudo-code of Scale-teaching. |
| Open Source Code | Yes | Our implementation of Scale-teaching is available at https://github.com/qianlima-lab/Scale-teaching. |
| Open Datasets | Yes | We use three time series benchmarks (four individual large datasets [3, 52, 53], UCR 128 archive [22], and UEA 30 archive [54]) for experiments. ... For detailed information about UCR datasets, please refer to https://www.cs.ucr.edu/~eamonn/time_series_data_ 2018/. ... For detailed information about UEA datasets, please refer to https: //www.timeseriesclassification.com/dataset.php. |
| Dataset Splits | No | Each UCR dataset includes a single training set and a single test set, and each time series sample has been z-normalized. ... Each dataset contains a partitioned training set and a test set. The paper mentions using the test set for evaluations and setting hyperparameters based on default settings from related works, rather than explicitly defining a separate validation split within their experimental setup. |
| Hardware Specification | Yes | Finally, we build our model using Py Torch 1.10 platform with 2 NVIDIA Ge Force RTX 3090 GPUs. |
| Software Dependencies | Yes | Finally, we build our model using Py Torch 1.10 platform with 2 NVIDIA Ge Force RTX 3090 GPUs. |
| Experiment Setup | Yes | The learning rate is set to 1e-3, the maximum batch size is set to 256, and the maximum epoch is set to 200. ewarm is set to 30 and eupdate is set to 90. α in Eq. 3 is set to 0.9, σ in Eq. 4 is set to 0.25, β in Eq. 5 is set to 0.99, the largest neighbor K is set to 10, and γ is set to 0.99. In addition, following the parameter settings suggested in [23], we linearly decay the learning rate to zero from the 80-th epoch to 200-th epoch. |