Contrastive Predictive Coding for Anomaly Detection
- URL: http://arxiv.org/abs/2107.07820v1
- Date: Fri, 16 Jul 2021 11:04:35 GMT
- Title: Contrastive Predictive Coding for Anomaly Detection
- Authors: Puck de Haan, Sindy L\"owe
- Abstract summary: Contrastive Predictive Coding model (arXiv:1807.03748) used for anomaly detection and segmentation.
We show that its patch-wise contrastive loss can directly be interpreted as an anomaly score.
Model achieves promising results for both anomaly detection and segmentation on the MVTec-AD dataset.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reliable detection of anomalies is crucial when deploying machine learning
models in practice, but remains challenging due to the lack of labeled data. To
tackle this challenge, contrastive learning approaches are becoming
increasingly popular, given the impressive results they have achieved in
self-supervised representation learning settings. However, while most existing
contrastive anomaly detection and segmentation approaches have been applied to
images, none of them can use the contrastive losses directly for both anomaly
detection and segmentation. In this paper, we close this gap by making use of
the Contrastive Predictive Coding model (arXiv:1807.03748). We show that its
patch-wise contrastive loss can directly be interpreted as an anomaly score,
and how this allows for the creation of anomaly segmentation masks. The
resulting model achieves promising results for both anomaly detection and
segmentation on the challenging MVTec-AD dataset.
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