This is the official implementation of the paper: Fence GAN: Towards Better Anomaly Detection (link).
- Linux OS
- Python 3
- CUDA
- Tensorflow Version 1.12 (Tested on this version)
-
Clone repository
git clone https://github.com/phuccuongngo99/Fence_GAN.git -
Installing tensorflow or tensorflow-gpu by following instruction here.
-
Installing necessary libraries
pip3 install -r requirements.txt
Check results and plots under result folder
python3 2D_experiment/2D_fgan.py
python3 main.py --dataset mnist --ano_class 0 --epochs 100 --alpha 0.1 --beta 30 --gamma 0.1 --batch_size 200 --pretrain 15 --d_lr 1e-5 --g_lr 2e-5 --v_freq 4 --latent_dim 200 --evaluation 'auprc'
python3 main.py --dataset cifar10 --ano_class 0 --epochs 150 --alpha 0.5 --beta 10 --gamma 0.5 --batch_size 128 --pretrain 15 --d_lr 1e-4 --g_lr 1e-3 --v_freq 1 --latent_dim 256 --evaluation 'auroc'
Unzip the KDD99_Final.zip and then run Fence_GAN.py. Hyperparameters are set as global variables in the Fence_GAN.py file
Enter python3 main.py -h for more training options
usage: Train your Fence GAN [-h] [--dataset DATASET] [--ano_class ANO_CLASS]
[--epochs EPOCHS] [--beta BETA] [--gamma GAMMA]
[--alpha ALPHA] [--batch_size BATCH_SIZE]
[--pretrain PRETRAIN] [--d_l2 D_L2] [--d_lr D_LR]
[--g_lr G_LR] [--v_freq V_FREQ] [--seed SEED]
[--evaluation EVALUATION]
[--latent_dim LATENT_DIM]
optional arguments:
-h, --help show this help message and exit
--dataset mnist | cifar10
--ano_class 1 anomaly class
--epochs number of epochs to train
--beta beta
--gamma gamma
--alpha alpha
--batch_size
--pretrain number of pretrain epoch
--d_l2 L2 Regularizer for Discriminator
--d_lr learning_rate of discriminator
--g_lr learning rate of generator
--v_freq epoch frequency to evaluate performance
--seed numpy and tensorflow seed
--evaluation 'auprc' or 'auroc'
--latent_dim Latent dimension of Gaussian noise input to Generator
@article{ngo2019,
author = {Cuong Phuc Ngo and Amadeus Aristo Winarto and Connie Khor Li Kou and
Sojeong Park and Farhan Akram and Hwee Kuan Lee},
title = {Fence GAN: Towards Better Anomaly Detection},
year = {2019},
url = {https://arxiv.org/pdf/1904.01209.pdf},
archivePrefix = {arXiv}
}