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 self- and 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