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研究生：	曹靖婕 Tsao, Ching-Chieh
論文名稱：	以時間對齊鬆弛策略改善深度學習在領域適應的應用 Unlocking Domain Adaptation through Relaxation of Temporal Alignment
指導教授：	蔣榮先 Chiang, Jung-Hsien
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2024
畢業學年度：	112
語文別：	英文
論文頁數：	89
中文關鍵詞：	領域適應、非監督式、時域對齊、時序資料、頻域
外文關鍵詞：	Domain Adaptation, Unsupervised Learning, Temporal Alignment, Time Series, Frequency Domain
ORCID：	0009-0008-6049-5195
相關次數：	點閱：43 下載：0
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中文摘要 i
Abstract iii
誌謝 vi
Contents viii
List of Tables xi
List of Figures xiii
Introduction 1
1 Background 1
2 Motivation 2
3 Research Objectives 3
4 Thesis Organization 4
Literature Review 5
1 Characteristics and Applications of Time Series 5
1.1 Time-series characteristics 5
1.2 Characteristics of time series in frequency domain 6
1.3 Applications of time series 8
2 Cross-Domain Learning Approaches 8
2.1 Transfer learning 9
2.2 Domain Generalization (DG) 10
2.3 Domain Adaptation (DA) 11
2.4 Unsupervised Domain Adaptation (UDA) 12
2.5 Time-series UDA 13
3 Summary 14
Unlocking Domain Adaptation through  Relaxation of Temporal Alignment 16
1 Overview 16
1.1 Vector Quantised-Variational AutoEncoder (VQ-VAE) 16
1.2 Self-supervised Reconstruction Learning 17
2 Problem Formulations for Time Series UDA 18
3 Dual Stream Encoder 20
4 Hierarchical Embedding Table (HET) 20
5 Summary 22
Advanced Techniques for Temporal and Spectral Feature Extraction 23
1 Nearest Neighbor Search Algorithm 23
2 Voting Mechanism 25
3 Objective Functions 26
3.1 Reconstruction Loss 26
3.2 Feature-embedding Consistency Loss 27
3.3 Dissimilarity Loss 28
3.4 Comprehensive Overview of the Loss Function 29
4 Summary 29
Experiments 33
1 Experimental Setting 33
1.1 UDA Benchmarks 33
1.2 Domain Generalization task 36
2 Main Results 37
2.1 Benchmark for UDA 37
2.2 Benchmark for Domain Generalization 38
3 Ablation Study 39
4 T-SNE Visualization 41
5 Hyperparameter Optimization 44
6 Clinical Task: IDH Prediction 44
7 Summary 45
Discussion 58
1 Explaining Methodological Outperformance 58
2 Classifier-free is efficient or not 59
3 Comparison of Multi-prototype Classifier to HET 59
4 How the integration of presumed domain-invariant properties in the frequency domain is achieved? 60
5 Will the spectral block be frozen to give better results? 60
6 Computation Analysis 62
6.1 Size of the hierarchical embedding table 62
6.2 Number of classification categories 63
7 Summary 64
Conclusion and Future Works 65
1 Conclusion 65
2 Future Works 66
References 68
                                    

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校內：2025-12-31公開
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