Self-Supervised Out-of-Distribution Detection and Localization with Natural Synthetic Anomalies (NSA)

• URL: http://arxiv.org/abs/2109.15222v1
• Date: Thu, 30 Sep 2021 15:50:04 GMT
• Title: Self-Supervised Out-of-Distribution Detection and Localization with Natural Synthetic Anomalies (NSA)
• Authors: Hannah M. Schl\"uter, Jeremy Tan, Benjamin Hou, Bernhard Kainz
• Abstract summary: We introduce a new self-supervised task, NSA, for training an end-to-end model for anomaly detection and localization. NSA uses Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. Our experiments with the MVTec AD dataset show that a model trained to localize NSA anomalies generalizes well to detecting real-world a priori unknown types of manufacturing defects.
• Score: 3.862647284311923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a new self-supervised task, NSA, for training an end-to-end model for anomaly detection and localization using only normal data. NSA uses Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. This creates a wide range of synthetic anomalies which are more similar to natural sub-image irregularities than previous data-augmentation strategies for self-supervised anomaly detection. We evaluate the proposed method using natural and medical images. Our experiments with the MVTec AD dataset show that a model trained to localize NSA anomalies generalizes well to detecting real-world a priori unknown types of manufacturing defects. Our method achieves an overall detection AUROC of 97.2 outperforming all previous methods that learn from scratch without pre-training datasets.

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