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Details
Table of Contents
Intro
Preface
Organization
Contents
Annotating Data
Active Learning for Reducing Labeling Effort in Text Classification Tasks
1 Introduction
2 Related Work
3 Methods
3.1 Active Learning
3.2 Model Architecture
3.3 Query Functions
3.4 Heuristics
3.5 Experimental Setup
4 Results
4.1 Active Learning
4.2 Query-Pool Size
4.3 Heuristics
5 Discussion
A.1 RET Algorithm Computational Cost Analysis
A.2 Algorithms
References
Refining Weakly-Supervised Free Space Estimation Through Data Augmentation and Recursive Training
1 Introduction
2 Related Work
2.1 Supervised Learning for Segmentation
2.2 Weakly-Supervised Semantic Segmentation
2.3 Unsupervised and Weakly-Supervised Monocular Free Space Segmentation
2.4 Training Strategies for Weakly-Supervised Segmentation
3 Methodology
3.1 Data Augmentation
3.2 Recursive Training
4 Experimental Setup
4.1 Dataset
4.2 Evaluation Metrics
4.3 Network Architectures
4.4 Training Procedure
4.5 Use of Ground Truth Data
5 Results
5.1 Fully-Supervised Results
5.2 Unsupervised and Weakly-Supervised Baselines
5.3 Data Augmentation and Recursive Training
5.4 Limits of Recursive Training
5.5 Qualitative Results
6 Conclusion
References
Self-labeling of Fully Mediating Representations by Graph Alignment
1 Introduction
2 Related Work
3 Self-labeling of Fully Mediating Representations
3.1 Graph Alignment
3.2 Method
4 Experiments
5 Conclusion
A Appendix
A.1 Architecture Summary of Graph Recognition Tool
A.2 Training Details for Graph Recognition Tool
A.3 Computational Cost per Rich-Labeling Iteration
A.4 Examples of Cases Where Graph Alignment Fails
3 Proposed Method
3.1 Adversarial Domain Adaptation for Object Detection
4 Implementation Details
5 Evaluation
5.1 Datasets
5.2 Experiments
6 Conclusion
References
Explaining Outcomes
Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities
Abstract
1 Introduction
2 Theoretical Background
3 Research Method
3.1 Use Cases
3.2 Data Collection
3.3 Data Analysis
4 Results
4.1 Consumer Credit
4.2 Credit Risk Management
4.3 Anti-money Laundering (AML)
4.4 General
5 Discussion and Conclusions
Preface
Organization
Contents
Annotating Data
Active Learning for Reducing Labeling Effort in Text Classification Tasks
1 Introduction
2 Related Work
3 Methods
3.1 Active Learning
3.2 Model Architecture
3.3 Query Functions
3.4 Heuristics
3.5 Experimental Setup
4 Results
4.1 Active Learning
4.2 Query-Pool Size
4.3 Heuristics
5 Discussion
A.1 RET Algorithm Computational Cost Analysis
A.2 Algorithms
References
Refining Weakly-Supervised Free Space Estimation Through Data Augmentation and Recursive Training
1 Introduction
2 Related Work
2.1 Supervised Learning for Segmentation
2.2 Weakly-Supervised Semantic Segmentation
2.3 Unsupervised and Weakly-Supervised Monocular Free Space Segmentation
2.4 Training Strategies for Weakly-Supervised Segmentation
3 Methodology
3.1 Data Augmentation
3.2 Recursive Training
4 Experimental Setup
4.1 Dataset
4.2 Evaluation Metrics
4.3 Network Architectures
4.4 Training Procedure
4.5 Use of Ground Truth Data
5 Results
5.1 Fully-Supervised Results
5.2 Unsupervised and Weakly-Supervised Baselines
5.3 Data Augmentation and Recursive Training
5.4 Limits of Recursive Training
5.5 Qualitative Results
6 Conclusion
References
Self-labeling of Fully Mediating Representations by Graph Alignment
1 Introduction
2 Related Work
3 Self-labeling of Fully Mediating Representations
3.1 Graph Alignment
3.2 Method
4 Experiments
5 Conclusion
A Appendix
A.1 Architecture Summary of Graph Recognition Tool
A.2 Training Details for Graph Recognition Tool
A.3 Computational Cost per Rich-Labeling Iteration
A.4 Examples of Cases Where Graph Alignment Fails
3 Proposed Method
3.1 Adversarial Domain Adaptation for Object Detection
4 Implementation Details
5 Evaluation
5.1 Datasets
5.2 Experiments
6 Conclusion
References
Explaining Outcomes
Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities
Abstract
1 Introduction
2 Theoretical Background
3 Research Method
3.1 Use Cases
3.2 Data Collection
3.3 Data Analysis
4 Results
4.1 Consumer Credit
4.2 Credit Risk Management
4.3 Anti-money Laundering (AML)
4.4 General
5 Discussion and Conclusions