Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification (CA-TCC) [Paper] [Cite]
The paper is accepted in the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
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.
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. The 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.
The codes of the semi-supervised learning baselines I used in the paper are HERE.
The codes of the self-supervised learning baselines I used in the paper can be found in my other work.
To run everything smoothly, we included ca_tcc_pipeline.sh
file. You can simply use it.
If you found this work useful for you, please consider citing it.
@inproceedings{tstcc,
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{catcc,
author={Eldele, Emadeldeen and Ragab, Mohamed and Chen, Zhenghua and Wu, Min and Kwoh, Chee-Keong and Li, Xiaoli and Guan, Cuntai},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification},
year={2023},
volume={},
number={},
pages={1-15},
doi={10.1109/TPAMI.2023.3308189}
}
Please contact me for any issues/questions regarding the paper or reproducing the results at: emad0002{at}e.ntu.edu.sg