A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method-Reference-Cited by-同舟云学术

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A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method

Published:2023 Issue: Volume:11 Page:127526-127538
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Chen Yibo¹,Peng Haolong²,Huang Le³,Zhang Jianming¹,Jiang Wei¹^ORCID

Affiliation:

1. College of Control Science and Engineering, Zhejiang University, Hangzhou, China

2. Zhejiang Polytechnic Institute, Zhejiang University, Hangzhou, China

3. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10316283.pdf?arnumber=10316283

Reference75 articles.

1. Prototypical Residual Networks for Anomaly Detection and Localization

2. Normal Reference Attention and Defective Feature Perception Network for Surface Defect Detection

3. Unsupervised Automatic Defect Inspection based on Image Matching and Local One-class Classification

4. MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities

5. Isolation Forest

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1. VDKD: A ViT-Based Student-Teacher Knowledge Distillation for Multi-Texture Class Anomaly Detection;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17

2. Implementing ViT Models for Traffic Sign Detection in Autonomous Driving Systems;2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST);2024-04-09

3. Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding;IEEE Access;2024

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