paper: https://doi.org/10.1016/j.neucom.2022.10.054.
CDIL-CNN is a novel convolutional model for sequence classification. We use symmetric dilated convolutions, a circular mixing protocol, and an average ensemble learning.
To reproduce the synthetic data experiment results, you should:
- Run xor_generation.py;
- Run xor_main.py for one model or run xor_all.sh for all models.
The generator will create 3 files for each sequence length and store them in the ./xor_datasets/ folder in the following format:
xor_{length}_train.pt
xor_{length}_test.pt
xor_{length}_val.pt
The ./xor_log/ folder will save all results. The ./xor_model/ folder will save all best models.
To reproduce the dissimilar experiment results, you should:
- Run dissimilar_generation.py;
- Run dissimilar_main.py for one model or run dissimilar_all.sh for all models.
The generator will create 4 files for sequence length of 2048 and store them in the ./dissimilar_datasets/ folder in the following format:
dissimilar_2048_train.pt
dissimilar_2048_test.pt
dissimilar_2048_val.pt
dissimilar_2048_dtest.pt
The ./dissimilar_log/ folder will save all results. The ./dissimilar_model/ folder will save all best models.
We provide our used configurations in syn_config.py.
Long Range Arena (LRA) is a public benchmark suite. The datasets and the download link can be found in the official GitHub repository.
To reproduce the LRA experiment results, you should:
- Download
lra_release.gz
(~7.7 GB), extract it, move the folder./lra_release/lra_release
into our ./create_datasets/ folder, and run all_create_datasets.sh. - Run lra_main.py for one experiment or run lra_all.sh for all experiments.
The dataset creators will create 3 files for each task and store them in the ./lra_datasets/ folder in the following format:
{task}.train.pickle
{task}.test.pickle
{task}.dev.pickle
The ./lra_log/ folder will save all results. The ./lra_model/ folder will save all best models.
We provide our used configurations in lra_config.py.
The UEA & UCR Repository consists of various time series classification datasets. We use three audio datasets: FruitFlies, RightWhaleCalls, and MosquitoSound.
To reproduce the time series results, you should:
- Download the datasets, extract them, move the extracted folders into our ./time_datasets/ folder, and run time_arff_generation.py.
- Run time_main.py or for one experiment or run time_all.sh for all experiments.
The generator will create 3 files for each dataset and store them in the ./time_datasets/ folder in the following format:
{dataset}_train.csv
{dataset}_val.csv
{dataset}_test.csv
The ./time_log/ folder will save all results. The ./time_model/ folder will save all best models.
To reproduce the noisy RightWhaleCalls results, you should:
- Run noise_generation.py;
- Run noise_main.py for one model or run noise_all.sh for all models.
The generator will create 8 files for each dataset and store them in the ./noise_datasets/ folder in the following format:
RightWhaleCalls_train_data.csv
RightWhaleCalls_train_label.csv
RightWhaleCalls_val_data.csv
RightWhaleCalls_val_label.csv
RightWhaleCalls_test_data.csv
RightWhaleCalls_test_label.csv
RightWhaleCalls_dtest_data.csv
RightWhaleCalls_dtest_label.csv
The ./noise_log/ folder will save all results. The ./noise_model/ folder will save all best models.
We provide our used configurations in time_config.py.