Anomaly Detection with Deep Compact Hypersphere Derin Kompakt Hiperküre Yöntemi ile Anomali Sezimi

Salk Y., Uzun B., ÇEVİKALP H., Saribas H.

30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 15 - 18 Mayıs 2022 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu55565.2022.9864994
  • Basıldığı Şehir: Safranbolu
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Anomaly detection, compact hypersphere, deep learning, Outlier Exposure
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.The classifiers that return compact acceptance regions are crucial for the success in anomaly detection. This paper introduces a novel method that approximates the class acceptance regions with compact hypersphere models for anomaly detection. As opposed to the other deep hypersphere classifiers, we treat the hypersphere centers as learnable parameters and update them based on the changing deep feature representations. In addition, we propose a novel loss term that is more robust to label noises within outlier exposure dataset. The experimental results show that the proposed method achieves the state-of-the-art accuracies on the majority of the tested datasets in the context of anomaly detection.