/Efficient-GAN-Anomaly-Detection

Primary LanguagePythonMIT LicenseMIT

Efficient-GAN-Based Anomaly Detection

Official implementation of the prepublished article submitted to the ICLRW 2018: https://arxiv.org/abs/1802.06222

NEW! Updated version of this work in "Adversarially Learned Anomaly Detection" paper!

Anomaly Detection materials, by the Deep Learning 2.0 team in I2R, A*STAR, Singapore

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{zenati2018,
  author    = {Houssam Zenati and
               Chuan Sheng Foo and
               Bruno Lecouat and
               Gaurav Manek and
               Vijay Ramaseshan Chandrasekhar},
  title     = {Efficient GAN-Based Anomaly Detection},
  year      = {2018},
  url       = {http://arxiv.org/abs/1802.06222},
  archivePrefix = {arXiv}
}

Prerequisites.

To run the code, follow those steps:

Install Python 3

sudo apt install python3 python3-pip

Download the project code:

git clone https://github.com/houssamzenati/Efficient-GAN-Anomaly-Detection

Install requirements (in the cloned repository):

pip3 install -r requirements.txt

Doing anomaly detection.

Running the code with different options

python3 main.py <gan, bigan> <mnist, kdd> run --nb_epochs=<number_epochs> --label=<0, 1, 2, 3, 4, 5, 6, 7, 8, 9> --w=<float between 0 and 1> --m=<'cross-e','fm'> --d=<int> --rd=<int>

To reproduce the results of the paper, please use w=0.1 (as in the original AnoGAN paper which gives a weight of 0.1 to the discriminator loss), d=1 for the feature matching loss.