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}
}
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
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.