This is the implementation of the GANomaly paper.
Model Type: Classification
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
The key idea here is that, during inference, when an anomalous image is passed through the first encoder the latent vector
python tools/train.py --model ganomaly
All results gathered with seed 42
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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 |
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 |