sashirin / efficient-gan-anomaly-detection

Efficient-GAN-Based Anomaly Detection

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