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Communication Dans Un Congrès Année : 2021

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

Ngoc-Son Vu
Jean Beaudet
  • Fonction : Auteur
  • PersonId : 1114110

Résumé

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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Dates et versions

hal-03387879 , version 1 (20-10-2021)

Identifiants

  • HAL Id : hal-03387879 , version 1

Citer

Loic Jezequel, Ngoc-Son Vu, Jean Beaudet, Aymeric Histace. Fine-grained Anomaly Detection via Multi-task Self-Supervision. AVSSP 2021, IEEE, Nov 2021, Washington (Virtual), United States. ⟨hal-03387879⟩
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