TY - GEN
T1 - Balancing Feature Imitation and Knowledge Consistency for Improving Anomaly Detection
AU - Wang, Zi
AU - Hotta, Katsuya
AU - Nakazawa, Kohei
AU - Zhang, Chao
AU - Yu, Jun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Unsupervised anomaly detection methods are essential for addressing the challenge of accessing anomalous data in real-world scenarios. Among these methods, student-teacher framework-based methods have garnered increasing attention due to their low latency and competitive performance. This framework leverages the knowledge disparities between the teacher and student networks, particularly their differences in feature extraction capabilities, for anomaly detection. In this paper, we propose not only to leverage the process of feature imitation for anomaly scoring but also to exploit the consistency among neighboring layer groups to enhance anomaly detection performance. Experiment shows that our model can significantly improve anomaly detection performance by incorporating a knowledge consistency term into the scoring process.
AB - Unsupervised anomaly detection methods are essential for addressing the challenge of accessing anomalous data in real-world scenarios. Among these methods, student-teacher framework-based methods have garnered increasing attention due to their low latency and competitive performance. This framework leverages the knowledge disparities between the teacher and student networks, particularly their differences in feature extraction capabilities, for anomaly detection. In this paper, we propose not only to leverage the process of feature imitation for anomaly scoring but also to exploit the consistency among neighboring layer groups to enhance anomaly detection performance. Experiment shows that our model can significantly improve anomaly detection performance by incorporating a knowledge consistency term into the scoring process.
UR - http://www.scopus.com/inward/record.url?scp=85213378843&partnerID=8YFLogxK
U2 - 10.1109/GCCE62371.2024.10760365
DO - 10.1109/GCCE62371.2024.10760365
M3 - 会議への寄与
AN - SCOPUS:85213378843
T3 - GCCE 2024 - 2024 IEEE 13th Global Conference on Consumer Electronics
SP - 1409
EP - 1410
BT - GCCE 2024 - 2024 IEEE 13th Global Conference on Consumer Electronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE Global Conference on Consumer Electronic, GCCE 2024
Y2 - 29 October 2024 through 1 November 2024
ER -