Deep SAD with Customized Dataset

This is repository modifying some part of the original Deep SAD code. The work is ongoing. Later I would simplify its structure for easier understanding.

Currently, major modifications include:

1. Adding the function (adding some classes in base, datasets, net, main and so on) to support custom datasets.
2. Adding a LSTM autoencoder to support learning of multivariate time series. (Currently you should change the dimensions in it.)
3. Adding a main_evaluation.py which provides a more flexible evaluation for the model. Basically, it loads a model and evaluates it on any data you choose.

Citation

You could find a preprint of the Deep Semi-Supervised Anomaly Detection paper on arXiv.

@article{ruff2019,
  title     = {Deep Semi-Supervised Anomaly Detection},
  author    = {Ruff, Lukas and Vandermeulen, Robert A. and G{\"o}rnitz, Nico and Binder, Alexander and M{\"u}ller, Emmanuel and M{\"u}ller, Klaus-Robert and Kloft, Marius},
  journal   = {arXiv preprint arXiv:1906.02694},
  year      = {2019}
}