Disentangled Knowledge Distillation for Unified Multi-Class Anomaly Detection

Ji-Yong Jang; Hayeon Lee; Younkwan Lee

doi:10.1109/icip51287.2024.10647795

ScienceGate Book Chapters

JOURNAL ARTICLE

Disentangled Knowledge Distillation for Unified Multi-Class Anomaly Detection

Ji-Yong Jang Hayeon Lee Younkwan Lee

Year: 2024 Pages: 312-318

DOI: 10.1109/icip51287.2024.10647795

Get Full-Text PDF Get Analytical Report

Keywords:

Distillation Anomaly detection Computer science Class (philosophy) Anomaly (physics) Artificial intelligence Machine learning Data mining Chemistry Chromatography

Metrics

Cited By

0.64

FWCI (Field Weighted Citation Impact)

Refs

0.69

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Advanced Malware Detection Techniques

Physical Sciences → Computer Science → Signal Processing

Disentangled Knowledge Distillation for Unified Multi-Class Anomaly Detection

Metrics

Citation History

Topics

Related Documents

A unified multi‐class anomaly detection model based on reverse distillation

Anomaly detection based on multi-teacher knowledge distillation

Knowledge Distillation Anomaly Detection with Multi-Scale Feature Fusion

MCAD: Multi-classification anomaly detection with relational knowledge distillation

Multi-teacher knowledge distillation framework for lightweight anomaly detection