Artificial neural networks and machine learning -- ICANN 2022 : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6-9, 2022, Proceedings. Part IV

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

The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected...

Full description

Saved in:
Bibliographic Details
Corporate Author: International Conference on Artificial Neural Networks (European Neural Network Society) Bristol, England ; Online)
Other Authors: Pimenidis, Elias (Editor), Angelov, Plamen P. (Editor), Jayne, Chrisina (Editor), Papaleonidas, Antonios (Editor), Aydin, M. E. (Mehmet E.) (Editor)
Format: eBook
Language:English
Published: Cham, Switzerland : Springer, 2022.
Series:Lecture notes in computer science ; 13532.
Subjects:
Neural networks (Computer science) > Congresses.
Machine learning > Congresses.
Artificial intelligence > Congresses.
Artificial intelligence
Machine learning
Neural networks (Computer science)
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • 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

Similar Items