dbbbbm / f-AnoGAN-PyTorch

Unofficial PyTorch implementation for f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

f-AnoGAN-PyTorch

An unofficial implementation of f-AnoGAN in PyTorch.

Reference

• Official TensorFlow implementation: https://github.com/tSchlegl/f-AnoGAN
• Paper: f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks https://www.sciencedirect.com/science/article/abs/pii/S1361841518302640
• WGAN-GP-PyTorch: https://github.com/jalola/improved-wgan-pytorch

Dataset

This implementation performs anomaly detection on CIFAR-10. In the common setting we treat one class of CIFAR-10 as normal class and other 9 classes as anomalies. You can specify which class is considered as normal when running fanogan.py in command line by setting the --class argument.

Usage

• Train a GAN

    python fanogan.py --stage 1 --class NORMAL_CLASS
  

• Train an encoder

    python fanogan.py --stage 2 --class NORMAL_CLASS
  

• Evaluation

    python fanogan.py --eval --class NORMAL_CLASS