![](bg7.png)
Table 5: Pixel-AUROC comparison on MVTEC AD :
MixedTeacher.
Category CutPaste PatchCore FastFlow Ours
carpet 98.3 98.9 99.1 99.0
tile 90.5 95.6 96.6 95.9
grid 97.5 98.7 99.2 97.5
wood 95.5 95 94.1 94.9
leather 99.5 99.3 99.6 99.4
Mean 96.2 97.5 97.7 97.3
Table 6: Image-AUROC comparison on BTAD:
MixedTeacher.
Category FastFlow Ours
1 (wood from btad) 96.0 97.0
5.4.3 Inference Time Results
In terms of inference speed, our MixedTeacher is 3x
slower than the reduced student since it used two
teacher networks and a more complex student archi-
tecture.
6 CONCLUSION
In this paper, we proposed two methods for effi-
cient unsupervised anomaly detection using the prin-
ciple of knowledge distillation applied to unsuper-
vised anomaly training. Both methods offer differ-
ent benefits. The reduced student proposes a high
speed texture anomaly detector with an AUROC per-
formance close to the state of the art, this method is
to be used in situations where inference time is the
most important priority (mobile device, low computa-
tional power, cost efficiency). The MixedTeacher pro-
pose the highest actual performance on anomaly de-
tection with a localisation close to the state of the art
on the MVTEC AD textures with still a fast inference.
This method is to be used in situations where perfor-
mance is the priority and the computational power is
big enough (monitoring server etc ...)
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