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Unsupervised Anomaly Detection Using Self-Supervised Learning with Knowledge Distillation

Title
Unsupervised Anomaly Detection Using Self-Supervised Learning with Knowledge Distillation
Alternative Title
지식 증류와 자기지도 학습을 이용한 비지도 이상 감지
Author(s)
Tashfeen Abdul Muqeet Bin
DGIST Authors
Tashfeen Abdul Muqeet BinSang Hyun ParkOkkyun Lee
Advisor
박상현
Co-Advisor(s)
Okkyun Lee
Issued Date
2023
Awarded Date
2023-02-01
Type
Thesis
Description
Anomaly Detection, Deep learning, Self-Supervised Learning, Knowledge Distillation
Table Of Contents
I. Introduction 1
II. Related Works 4
1 Reconstruction-based anomaly detection 4
2 Representation-based anomaly detection 5
III. Proposed Framework 7
1 Overview 7
2 Feature Extraction 8
3 CutPaste Augmentation 8
4 Knowledge Distillation with Contrastive Task 9
5 Anomaly Scoring Metric 12
6 Inference 12
IV. Experiments and Results 14
1 Experimental setup 14
1.1 Datasets 14
1.2 Implementation Details 15
1.3 Baselines 16
2 Comparison with State-of-the-Art Methods 17
2.1 MVTec-LOCO dataset 17
2.2 MVTec-AD dataset 18
2.3 CIFAR10 dataset 19
V. Ablation Study 21
1 Task Influence 21
2 Multi-Head Attention Influence 21
3 CutPaste Variants Influence 22
4 Contrastive Transformations influence 22
VI. Discussion 24
VII. Conclusion 25
References 26
URI
http://hdl.handle.net/20.500.11750/45709

http://dgist.dcollection.net/common/orgView/200000653398
DOI
10.22677/THESIS.200000653398
Degree
Master
Department
Department of Robotics and Mechatronics Engineering
Publisher
DGIST
Related Researcher
  • 박상현 Park, Sang Hyun 로봇및기계전자공학과
  • Research Interests 컴퓨터비전; 인공지능; 의료영상처리
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Department of Robotics and Mechatronics Engineering Theses Master

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