Artificial neural networks and machine learning -- ICANN 2022 : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6-9, 2022, Proceedings. Part IV / Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin (eds.).

Intro
Preface
Organization
Contents - Part IV
Analysing the Predictivity of Features to Characterise the Search Space
1 Introduction
2 Related Work
3 Landscape Features
4 Experimental Results
4.1 Feature Exploratory Analysis
4.2 Operator Classification
5 Conclusions and Future Work
References
Boosting Feature-Aware Network for Salient Object Detection
1 Introduction
2 Related Work
3 Proposed Model
3.1 Overall Framework
3.2 Edge Guidance Sub-network
3.3 Object Sub-network
3.4 Loss Function
4 Experimental Results

4.1 Datasets and Evaluation Metrics
4.2 Implementation Details
4.3 Comparison with the State-of-the-Arts
4.4 Ablation Studies
5 Conclusion
References
Continual Learning Based on Knowledge Distillation and Representation Learning
1 Introduction
2 Related Works
2.1 Class Incremental Learning
2.2 Beta-VAE
2.3 Knowledge Distillation
3 Model and Methodology
3.1 KRCL Model
3.2 KRCL Loss Function
3.3 Model Parameters and Update Rules
4 Experimental Comparison
4.1 Benchmark Datasets
4.2 Baseline Methods
4.3 Network Architecture

4.4 Evaluation Metrics
4.5 Experimental Results and Analysis
5 Conclusions and Future Works
References
Deep Feature Learning for Medical Acoustics
1 Introduction
2 The Considered Frontends
2.1 Mel-filterbanks
2.2 LEAF
2.3 nnAudio
3 Models
3.1 EfficientNet
3.2 VGG
4 Datasets
4.1 Respiratory Dataset
4.2 Heartbeat Dataset
5 Experiments
5.1 Pre-processing
5.2 System Parameterization
6 Results
6.1 Test 1
Respiratory
6.2 Test 2
Heartbeat
6.3 Overall
7 Conclusion
References
Feature Fusion Distillation
1 Introduction

2 Related Work
3 Method
3.1 Feature Fusion Module
3.2 Asymmetric Switch Function
3.3 Total Loss Function
4 Experiments
4.1 Image Classification (CIFAR-100)
4.2 Image Classification (ImageNet-1K)
4.3 Object Detection
4.4 Semantic Segmentation
5 Ablation Study
6 Conclusion
A Margin Value
References
Feature Recalibration Network for Salient Object Detection
1 Introduction
2 Proposed Method
2.1 Consistency Recalibration Module
2.2 Multi-source Feature Recalibration Module
2.3 Loss Function
3 Experiments
3.1 Datasets and Evaluation Metrics

3.2 Implementation Details
3.3 Comparison with the State-of-the-Art
3.4 Ablation Studies
4 Conclusion
References
Feature Selection for Trustworthy Regression Using Higher Moments
1 Introduction
2 Trustworthy Regression
3 Feature Relevance
3.1 Feature Relevance for Classification
3.2 Feature Relevance for (MSE-)Regression
4 Feature Selection Methods
5 On the Relation of Relevance Notions
6 Application: Moment Feature Relevance
7 Empirical Evaluation
8 Conclusion
References
Fire Detection Based on Improved-YOLOv5s
1 Introduction
2 Method