Fine-grained Anomaly Detection via Multi-task Self-Supervision
This work addresses the problem of detecting subtle anomalies in various fields, offering a significant improvement over existing self-supervised methods.
The paper tackled fine-grained anomaly detection by combining high-scale shape features and low-scale fine features in a multi-task self-supervised framework, resulting in up to 31% relative error reduction in AUROC compared to state-of-the-art methods.
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.