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Detecting anything overlooked in semantic segmentation / submitted by Robin Chan. Wuppertal, February 8, 2022
Content
Introduction
Theoretical Foundation
Feedforward Neural Networks
Training of Neural Networks
Convolutional Neural Networks
Semantic Segmentation
Real-World Applications
Public Datasets and Benchmarks
Evolution of Neural Network Architectures
Evaluation Metrics
Bibliography
False Negative Detection and Reduction in Semantic Segmentation
Cost-based Decision Rules
Maximum Likelihood Decision Rules
Prediction Error Meta Classification
Controlled False-Negative Reduction of Minority Classes
Out-of-Distribution Detection in Semantic Segmentation
Softmax Entropy Thresholding
Entropy Maximization and Meta Classification
Benchmark for the Segmentation of Out-of-Distribution Objects
Publications
The Ethical Dilemma of Cost-based Decision Rules
Maximum Likelihood Decision Rules for Handling Class Imbalance
Prediction Error Meta Classification
Controlled False Negative Reduction of Minority Classes
Detecting Out-of-Distribution Objects via Softmax Entropy Thresholding
Entropy Maximization and Meta Classification for Out-of-Distribution Detection
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
Conclusion
List of Figures
Notations and Symbols
Definitions, Propositions, Lemmas and Theorems