- Intro
- Preface
- Organization
- Contents
- Invited Papers
- What's Wrong with Computer Vision?
- 1 Introduction
- 2 Top Ten Questions a Theory on Vision Should Address
- 3 Hierarchical Description of Visual Tasks
- 3.1 Pixel-Wise and Abstract Visual Interpretations
- 3.2 The Interwound Story of Vision and Language
- 3.3 When Vision Collapses to Classification
- 4 Conclusions
- References
- Deep Learning in the Wild
- 1 Introduction
- 2 Face Matching
- 3 Print Media Monitoring
- 4 Visual Quality Control
- 5 Music Scanning
- 6 Game Playing
- 7 Automated Machine Learning
- 8 Conclusions
- References
- Learning Algorithms and Architectures
- Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines
- 1 Introduction
- 2 Least Squares Support Vector Machines
- 3 Large Margin Distribution Machines and Their Variants
- 3.1 Large Margin Distribution Machines
- 3.2 Least Squares Large Margin Distribution Machines
- 3.3 Unconstrained Large Margin Distribution Machines
- 4 Performance Evaluation
- 4.1 Conditions for Experiment
- 4.2 Results for Two-Class Problems
- 5 Conclusions
- References
- DLL: A Fast Deep Neural Network Library
- 1 Introduction
- 2 DLL: Deep Learning Library
- 2.1 Performance
- 2.2 Example
- 3 Experimental Evaluation
- 4 MNIST
- 4.1 Fully-Connected Neural Network
- 4.2 Convolutional Neural Network
- 5 CIFAR-10
- 6 ImageNet
- 7 Conclusion and Future Work
- References
- Selecting Features from Foreign Classes
- 1 Introduction
- 2 Methods
- 2.1 Learning from Context Classes
- 2.2 Foreign Class Combinations
- 3 Experiments
- 3.1 Datasets
- 4 Results
- 5 Discussion and Conclusion
- References
- A Refinement Algorithm for Deep Learning via Error-Driven Propagation of Target Outputs
- 1 Introduction
- 2 Error-Driven Target Propagation: Formalization of the Algorithms
- 2.1 The Inversion Net
- 2.2 Refinement of Deep Learning via Target Propagation
- 3 Experiments
- 4 Conclusions
- References
- Combining Deep Learning and Symbolic Processing for Extracting Knowledge from Raw Text
- 1 Introduction
- 2 Model
- 2.1 Semantic Features
- 2.2 Logic Constraints
- 2.3 Segmentation
- 3 Experiments
- 4 Conclusions
- References
- SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation
- 1 Introduction
- 2 Related Work
- 3 Proposed Method
- 4 Experiments
- 4.1 Network Architecture
- 4.2 Training Methodology
- 4.3 Isolated Learning
- 4.4 Adding New Tasks to the Models
- 4.5 Three Tasks Scenario
- 5 Conclusion
- References
- Classification Uncertainty of Deep Neural Networks Based on Gradient Information
- 1 Introduction
- 2 Entropy, Softmax Baseline and Gradient Metrics
- 3 Meta Classification
- A Benchmark Between Maximum Softmax Probability and Gradient Metrics
- 4 Recognition of Unlearned Concepts
- 5 Meta Classification with Known Unknowns
- 6 Conclusion and Outlook
- References
This book constitutes the refereed proceedings of the 8th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2018, held in Siena, Italy, in September 2018. The 29 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The papers present and discuss the latest research in all areas of neural network- and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
(source: Nielsen Book Data)
