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

• URL: http://arxiv.org/abs/2104.09993v1
• Date: Tue, 20 Apr 2021 14:19:08 GMT
• Title: Fine-grained Anomaly Detection via Multi-task Self-Supervision
• Authors: Loic Jezequel, Ngoc-Son Vu, Jean Beaudet, Aymeric Histace
• Abstract summary: Self-supervised learning has greatly helped many methods including anomaly detection. 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.
• Score: 2.9237210794416755
• License: http://creativecommons.org/licenses/by/4.0/
• 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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