f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

TensorFlow implementation of f-AnoGAN with MNIST dataset [1].
The base model WGAN is also implemented with TensorFlow.

Summary

f-AnoGAN architecture

The architecture of f-AnoGAN [1].

The logic for calculating anomaly score [1].

Graph in TensorBoard

Graph of f-AnoGAN.

Problem Definition

'Class-1' is defined as normal and the others are defined as abnormal.

Results

Training Phase-1 (WGAN Training)

Training graph of Phase-1

The rear half of the graph represents the state of the training phase 2.

Term Real	Term Fake

Loss D (Discriminator)	Loss G (Generator)

Result of Phase-1

z:2	z:2 (latent space walking)

z:64	z:128

Training Phase-2 (izi Training)

Training graph of Phase-2

The front half of the graph represents the state of the training phase 1.

Term izi	Term ziz	Loss E (Encoder)

Result of Phase-2

Restoration result by f-AnoGAN.

Test Procedure

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.

Environment

• Python 3.7.4
• Tensorflow 1.14.0
• Numpy 1.17.1
• Matplotlib 3.1.1
• Scikit Learn (sklearn) 0.21.3

Reference

[1] Schlegl, Thomas, et al (2019). f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical image analysis 54 (2019): 30-44.