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

2021-09-30 15:50:04
Hannah M. Schlüter, Jeremy Tan, Benjamin Hou, Bernhard Kainz
arXiv_CV
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/2109.15222

PDF

https://arxiv.org/pdf/2109.15222.pdf

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