Title

Unsupervised Anomaly Detection Using Self-Supervised Learning with Knowledge Distillation

Alternative Title

지식 증류와 자기지도 학습을 이용한 비지도 이상 감지

DGIST Authors

Tashfeen Abdul Muqeet Bin ; Sang Hyun Park ; Okkyun Lee

Advisor

박상현

Co-Advisor(s)

Okkyun Lee

Issued Date

2023

Awarded Date

2023-02-01

Citation

Tashfeen Abdul Muqeet Bin. (2023). Unsupervised Anomaly Detection Using Self-Supervised Learning with Knowledge Distillation. doi: 10.22677/THESIS.200000653398

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

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