GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

This is the implementation of the GANomaly paper.

Model Type: Classification

Description

GANomaly uses the conditional GAN approach to train a Generator to produce images of the normal data. This Generator consists of an encoder-decoder-encoder architecture to generate the normal images. The distance between the latent vector $z$ between the first encoder-decoder and the output vector $\hat{z}$ is minimized during training.

The key idea here is that, during inference, when an anomalous image is passed through the first encoder the latent vector $z$ will not be able to capture the data correctly. This would leave to poor reconstruction $\hat{x}$ thus resulting in a very different $\hat{z}$. The difference between $z$ and $\hat{z}$ gives the anomaly score.

Architecture

Usage

python tools/train.py --model ganomaly

Benchmark

All results gathered with seed 42.

MVTec AD Dataset

Image-Level AUC

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
	0.421	0.203	0.404	0.413	0.408	0.744	0.251	0.457	0.682	0.537	0.270	0.472	0.231	0.372	0.440	0.434

Image F1 Score

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
	0.834	0.864	0.844	0.852	0.836	0.863	0.863	0.760	0.905	0.777	0.894	0.916	0.853	0.833	0.571	0.881