TensorFlow implementation of f-AnoGAN with MNIST dataset [1].
The base model WGAN is also implemented with TensorFlow.
The architecture of f-AnoGAN [1].
The logic for calculating anomaly score [1].
Graph of f-AnoGAN.
'Class-1' is defined as normal and the others are defined as abnormal.
The rear half of the graph represents the state of the training phase 2.
| Term Real | Term Fake |
|---|---|
 |
 |
| Loss D (Discriminator) | Loss G (Generator) |
|---|---|
 |
 |
| z:2 | z:2 (latent space walking) |
|---|---|
 |
 |
| z:64 | z:128 |
|---|---|
 |
 |
The front half of the graph represents the state of the training phase 1.
| Term izi | Term ziz | Loss E (Encoder) |
|---|---|---|
 |
 |
 |
Restoration result by f-AnoGAN.
Box plot with encoding loss of test procedure.
Normal samples classified as normal.
Abnormal samples classified as normal.
Normal samples classified as abnormal.
Abnormal samples classified as abnormal.
- Python 3.7.4
- Tensorflow 1.14.0
- Numpy 1.17.1
- Matplotlib 3.1.1
- Scikit Learn (sklearn) 0.21.3
[1] Schlegl, Thomas, et al (2019). f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical image analysis 54 (2019): 30-44.