Feature Reconstruction via Reverse Distillation for Multi-class Anomaly Detection

Jiaxiang Wang; Huiming Xu; Zheyuan Cai; Tu Xiao-Lin

doi:10.1007/978-981-96-9921-6_26

ScienceGate Book Chapters

BOOK-CHAPTER

Feature Reconstruction via Reverse Distillation for Multi-class Anomaly Detection

Jiaxiang Wang Huiming Xu Zheyuan Cai Tu Xiao-Lin

Year: 2025 Lecture notes in computer science Pages: 316-328 Publisher: Springer Science+Business Media

DOI: 10.1007/978-981-96-9921-6_26

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Anomaly detection Distillation Class (philosophy) Feature (linguistics) Pattern recognition (psychology) Artificial intelligence Anomaly (physics) Data mining Chromatography Chemistry

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.39

Citation Normalized Percentile

Is in top 1%

Is in top 10%

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

Artificial Immune Systems Applications

Physical Sciences → Engineering → Biomedical Engineering

Feature Reconstruction via Reverse Distillation for Multi-class Anomaly Detection

Metrics

Topics

Related Documents

RD-RE: Reverse Distillation with Feature Reconstruction Enhancement for Industrial Anomaly Detection

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

Multi-Scale Feature Distillation for Anomaly Detection

Masked feature reconstruction distillation for unsupervised anomaly detection

Feature Enhancement With Reverse Distillation for Hyperspectral Anomaly Detection