The code for reproducing the results in the paper "Do autoencoders need a bottleneck for anomaly detection?"

Runs with baetorch (built on Pytorch), and Neural Tangent Kernel.

• Code for preprocessing benchmark datasets and industrial datasets are in benchmark and case_study folders, respectively.
• Code for analysing the results are in analyse folder.

Main codes are in the base folder:

• 01-Main-Run.py executes the training and prediction with deterministic AE, VAE and BAEs, and hyperparameter grids are specified in Params_{Dataset}.py (e.g. Params_ZEMA.py for ZeMA dataset).
• 02-Run-InfiniteBAE.py executes similar evaluation run but with infinitely wide BAE using Neural Tangent Kernel.
• The bottleneck can be adjusted by setting the latent_factor or skip hyperparameters in the grids.
• The main code creates a new experiments folder and save the results in csv files.

Yong, Bang Xiang, and Alexandra Brintrup. "Do autoencoders need a bottleneck for anomaly detection?." arXiv preprint arXiv:2202.12637 (2022).

@article{yong2022autoencoders,
  title={Do autoencoders need a bottleneck for anomaly detection?},
  author={Yong, Bang Xiang and Brintrup, Alexandra},
  journal={arXiv preprint arXiv:2202.12637},
  year={2022}
}