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.
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
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.