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arXiv · 2104.09993

Fine-grained Anomaly Detection via Multi-task Self-Supervision

Loic Jezequel·Ngoc-Son Vu·Jean Beaudet·Aymeric Histace
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Abstract

Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack finer features. By combining in a multi-task framework high-scale shape features oriented task with low-scale fine features oriented task, our method greatly improves fine-grained anomaly detection. It outperforms state-of-the-art with up to 31% relative error reduction measured with AUROC on various anomaly detection problems.

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Loic Jezequel, Ngoc-Son Vu, Jean Beaudet, Aymeric Histace. 2021-04-20. Fine-grained Anomaly Detection via Multi-task Self-Supervision. https://doi.org/10.1109/avss52988.2021.9663783

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