f-AnoGAN
f-AnoGAN is a GAN for anomaly detection. One of the features of this GAN is that two adversarial networks (Generator and Discriminator) and Encoder are trained separately. In addition, an anomaly score is computed by both a discriminator feature residual error and an image reconstruction error.
References
Papar
Github
- tSchlegl/f-AnoGAN: Code for reproducing f-AnoGAN training and anomaly scoring
- PyTorch-GAN/wgan_gp.py at master · eriklindernoren/PyTorch-GAN
fanogan/model.py, fanogan/train_wgangp.py and fanogan/train_encoder_izif.py are modified eriklindernoren's wgan_gp.py for f-AnoGAN.
Requirements
Python 3.6 or later
PyTorch 1.x
Matplotlib
Numpy
pandas
scikit-learn
Usage
Please run below in order.
Step: 0
python setup.py install
cd mnist
Step: 1
python train_wgangp.py --training_label 1
Step: 2
python train_encoder_izif.py --training_label 1
Step: 3
python test_anomaly_detection.py --training_label 1
After Step: 3, score.csv will be generated in the directory results.
See visualization.ipynb about data visualization for score.csv.