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

Artificial neural networks and machine learning -- ICANN 2022 : Part I / 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...

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Bibliographic Details
Corporate Author: International Conference on Artificial Neural Networks (European Neural Network Society) Bristol, England ; Online)
Other Authors: Pimenidis, Elias, Angelov, Plamen P., Jayne, Chrisina, Papaleonidas, Antonios, Aydin, M. E. (Mehmet E.)
Format: eBook
Language:English
Published: Cham : Springer, 2022.
Series:Lecture notes in computer science ; 13529.
Subjects:
Neural networks (Computer science) > Congresses.
Machine learning > Congresses.
Artificial intelligence > Congresses.
Artificial intelligence
Machine learning
Neural networks (Computer science)
Electronic books.
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 I
  • A Novel Deep Learning Based Method for Doppler Spectral Curve Detection
  • 1 Introduction
  • 2 Proposed Method
  • 2.1 Preprocessing and Coarse Segmentation
  • 2.2 Curve Correction
  • 2.3 Curve Filling
  • 2.4 Curve Fusion
  • 2.5 Objective Function
  • 3 Experiment
  • 3.1 Dataset
  • 3.2 Implementation Details
  • 3.3 Quantitative Evaluation
  • 3.4 Performance Evaluation of Proposed Model Combined with U-net
  • 3.5 Comparison of Model Efficiency
  • 4 Conclusion
  • References
  • A Unified Multiple Inducible Co-attentions and Edge Guidance Network for Co-saliency Detection
  • 1 Introduction
  • 2 Proposed Method
  • 2.1 Classified Inducible Co-Attention
  • 2.2 Focal Inducible Co-attention
  • 2.3 Individual Extraction
  • 2.4 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
  • Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
  • 1 Introduction
  • 2 Related Work
  • 2.1 Attention Mechanism for SOD
  • 2.2 Salient Object Detection for RSIs
  • 3 Approach
  • 3.1 Feature Encoding Backbone
  • 3.2 Position Enhancement Stage
  • 3.3 Detail Refinement Stage
  • 3.4 Hybrid Loss of AGNet
  • 4 Experiment
  • 4.1 Implementation Details
  • 4.2 Ablation Studies
  • 4.3 Comparison with State-of-the-arts
  • 5 Conclusion
  • References
  • BiSMSM: A Hybrid MLP-Based Model of Global Self-Attention Processes for EEG-Based Emotion Recognition
  • 1 Introduction
  • 2 Related Work
  • 3 Methods
  • 3.1 Spatial/Temporal Stream
  • 3.2 MLP-SA Mixer
  • 4 Experiments
  • 4.1 Datasets
  • 4.2 Experimental Setup
  • 4.3 Performance
  • 5 Conclusion
  • References
  • Boosting Both Robustness and Hardware Efficiency via Random Pruning Mask Selection
  • 1 Introduction
  • 2 Background and Related Works
  • 2.1 Adversarial Attacks and Adversarial Training
  • 2.2 Double-Win Network Pruning
  • 3 Method
  • 3.1 Random Mask Selection (RMS) Strategy
  • 3.2 Iterative Retraining Framework
  • 3.3 Hardware-Aware RMS (HW-RMS)
  • 4 Experiments
  • 4.1 Experiment Setup
  • 4.2 Benchmark with SOTA Methods
  • 4.3 Comparison with RST
  • 4.4 Ablation Study of FSP and Adversarial Gaps
  • 5 Conclusion

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