wefwefwef2 / ca-tcc

Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification

Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification (CA-TCC) [Paper] [Cite]

This work is an extention to TS-TCC, so if you need any details about the unsupervised pretraining and/or the datasets and its preprocessing, please check it first.

Training modes:

CA-TCC has two new training modes over TS-TCC

• "gen_pseudo_labels": which generates pseudo labels from fine-tuned TS-TCC model. This mode assumes that you ran "ft_1per" mode first.
• "SupCon": which performs supervised contrasting on pseudo-labeled data.

Note that "SupCon" is case-sensitive.

To fine-tune or linearly evaluate "SupCon" pretrained model, include it in the training mode. For example: "ft_1per" will fine-tune the TS-TCC pretrained model with 1% of labeled data. "ft_SupCon_1per" will fine-tune the CA-TCC pretrained model with 1% of labeled data. Same applies to "tl" or "train_linear".

To generate the 1%, you just need to split the data into 1%-99% and take the 1%. Also, you can find a script that does a similar job here. However, note that it creates it for 5-fold, so you can set it to just 1-fold.

Training procedure

To run everything smoothly, we included ca_tcc_pipeline.sh file. You can simply use it.

Citation

If you found this work useful for you, please consider citing it.

@inproceedings{ijcai2021-324,
  title     = {Time-Series Representation Learning via Temporal and Contextual Contrasting},
  author    = {Eldele, Emadeldeen and Ragab, Mohamed and Chen, Zhenghua and Wu, Min and Kwoh, Chee Keong and Li, Xiaoli and Guan, Cuntai},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI-21}},
  pages     = {2352--2359},
  year      = {2021},
}

@article{emadeldeen2022catcc,
  title   = {Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification},
  author  = {Eldele, Emadeldeen and Ragab, Mohamed and Chen, Zhenghua and Wu, Min and Kwoh, Chee Keong and Li, Xiaoli and Guan, Cuntai},
  journal = {arXiv preprint arXiv:2208.06616},
  year    = {2022}
}

Contact

For any issues/questions regarding the paper or reproducing the results, please contact me.
Emadeldeen Eldele
School of Computer Science and Engineering (SCSE),
Nanyang Technological University (NTU), Singapore.
Email: emad0002{at}e.ntu.edu.sg