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Visual Recognition Using Deep Neural Networks on Abstract and Decomposed Data / submitted by Annika Mütze, M.Sc. Wuppertal, 29.02.2024
Inhalt
Acknowledgments
Foreword
Introduction
Fundamentals
Deep Neural Networks
Feedforward Networks
Convolutional Neural Networks
Learning from Data
Statistical Learning
Error Decomposition
Universal Approximation
Training Process
Training Stabilization by Normalization and Regularization Layers
Deep Learning for Images
Classification
Semantic Segmentation
Overcoming Data Limitation
Data Augmentation
Weak and Semi-supervised Learning
Transfer Learning
Domain Adaptation
Generative Learning
Generative Adversarial Networks
Vanilla GAN
GAN Variants
Evaluation Metrics
Image-to-Image Translation
Pix-to-Pix
CycleGAN
Semi-supervised Task Aware Image-to-Image Domain Adaptation
Introduction
Related Work
Semi-supervised Task Aware I2I Translation
Stage a) – Training the Task Expert
Stage b) – Unsupervised Image-to-Image Translation
Stage c) – Downstream Task Awareness
Complete SSDA Method
Sampling Strategies for the Labeled Subset TR
Evaluation
Semantic Segmentation of Real and Simulated Street Scenes
Active SSDA on Real to Abstract Data
Conclusion and Outlook
Cooperation Is All You Need?
Introduction
Related Work
Cue Decomposition
Color
Texture
Shape
Cue Experts
Late Fusion
Cue Influence Analysis
Base Datasets
Implementation
Experimental Setup and Evaluation Metrics
Cityscapes Experiments
CARLA Experiments
Domain Shift Due to Cue Reduction
Influence on Class Level
Influence on the per Pixel Level
Influence of the Architecture: Transformer Experiments
Likelihood Based Evaluation
Conclusion, Discussion and Outlook
Discussion and Outlook
List of Notations