Zhangkai Wu, Longbing Cao, Qi Zhang, Junxian Zhou, Hui Chen
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Python 3.7
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PyTorch 1.1
- infoNCE Loss
# GD
# contrast
python main.py --train --seed 3 --model_name VQRAEcontrast --log_name NCE --loss_function mse --lmbda 0.0001 --use_clip_norm --batch_size 64 --preprocessing --dataset GD
# HSS
# contrast
python main.py --train --seed 3 --model_name VQRAEcontrast --log_name NCE --loss_function mse --lmbda 0.0001 --use_clip_norm --batch_size 64 --preprocessing --dataset HSS
# TD
## contrast
python main.py --train --seed 3 --model_name VQRAEcontrast --log_name NCE --loss_function mse --lmbda 0.0001 --use_clip_norm --batch_size 64 --preprocessing --dataset TD
- AdversirialLoss
# GD
python main.py --train --seed 3 --model_name VQRAEcontrast --log_name Discriminator --loss_function mse --lmbda 0.0001 --use_clip_norm --batch_size 64 --preprocessing --dataset GD --discriminator
# HSS
# discriminator
python main.py --train --seed 3 --model_name VQRAEcontrast --log_name Discriminator --loss_function mse --lmbda 0.0001 --use_clip_norm --batch_size 64 --preprocessing --dataset HSS --discriminator
# ECG
# ECG contrast
python main.py --train --seed 3 --model_name VQRAEcontrast --log_name NCE --loss_function mse --lmbda 0.0001 --use_clip_norm --batch_size 64 --preprocessing --dataset ECG
# ECG discriminator
# TD
# discriminator
python main.py --train --seed 3 --model_name VQRAEcontrast --log_name Discriminator --loss_function mse --lmbda 0.0001 --use_clip_norm --batch_size 64 --preprocessing --dataset TD --discriminator@article{wu2024weakly,
title={Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection},
author={Wu, Zhangkai and Cao, Longbing and Zhang, Qi and Zhou, Junxian and Chen, Hui},
journal={arXiv preprint arXiv:2401.03341},
year={2024}
}
Our codes are influenced by the following repos: VLDB22 and CIKM20