adversarially-learned-anomaly-detection's Introduction

Adversarially-Learned-Anomaly-Detection

ALAD (Proceedings of IEEE ICDM 2018) official code

The code for the paper "Adversarially Learned Anomaly Detection" (authors: Houssam Zenati*, Manon Romain*, Chuan Sheng Foo*, Bruno Lecouat, Vijay Ramaseshan Chandrasekhar) is now open source!

Please reach us via emails or via github issues for any enquiries!

Please cite our work if you find it useful for your research and work.

@article{Zenati2018AdversariallyLA,
  title={Adversarially Learned Anomaly Detection},
  author={Houssam Zenati and Manon Romain and Chuan Sheng Foo and Bruno Lecouat and Vijay R. Chandrasekhar},
  journal={2018 IEEE International Conference on Data Mining (ICDM)},
  year={2018},
  pages={727-736}
}

Prerequisites.

To run the code, follow those steps:

Download the project code:

git clone https://github.com/houssamzenati/Adversarially-Learned-Anomaly-Detection.git

Install requirements (in the cloned repository):

pip3 install -r requirements.txt

Doing anomaly detection.

Running the code with different options

python3 main.py <model> <dataset> run --nb_epochs=<number_epochs> --label=<0, 1, 2, 3, 4, 5, 6, 7, 8, 9> --m=<'cross-e','fm'> --d=<int> --rd=<int> etc.

Please refer to the argument parser in main.py for more details.

When using alad, please use it with --sn, --enable_early_stop and --enable_dzz. (Different options are provided to enable the ablation study). Important: we also provide implementations of DSEBM and DAGMM methods as open source work, the results reported in our paper for those methods, however, are derived from the DAGMM paper.

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