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|a 10.1007/978-3-031-15919-0
|2 doi
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|z (OCoLC)1374610268
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|a International Conference on Artificial Neural Networks (European Neural Network Society)
|n (31st :
|d 2022 :
|c Bristol, England ; Online)
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|a Artificial neural networks and machine learning -- ICANN 2022 :
|b 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6-9, 2022, Proceedings.
|n Part I /
|c Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin (eds.).
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3 |
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|a ICANN 2022
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| 260 |
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|a Cham :
|b Springer,
|c 2022.
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|a 1 online resource (783 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
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|0 http://rdaregistry.info/termList/fileType/1002
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|a Lecture notes in computer science ;
|v 13529
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|a 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
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| 505 |
8 |
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|a 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
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| 505 |
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|a 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
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|a 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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| 504 |
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|a References-Brain Tumor Segmentation Framework Based on Edge Cloud Cooperation and Deep Learning-1 Introduction-2 Related Works-3 Methods-3.1 The Workflow of ECC-BTSD-3.2 The Edge End of ECC-BTSD-3.3 The Cloud End of ECC-BTSD-4 Experiments-4.1 The Dataset and Preprocessing-4.2 Performance of ECC-BSTD in the Offline Mode-4.3 Performance of ECC-BSTD in the Online Mode-4.4 ECC-BSTD Analysis Under Edge-Cloud Collaboration-5 Conclusion-References-CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries-1 Introduction
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|a 2 Related Work
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|a 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 from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.
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|a Includes author index.
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| 588 |
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|a Online resource; title from PDF title page (SpringerLink, viewed September 14, 2022).
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| 650 |
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|a Neural networks (Computer science)
|v Congresses.
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| 650 |
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|a Machine learning
|v Congresses.
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|a Artificial intelligence
|v Congresses.
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|a Artificial intelligence
|2 fast
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|a Machine learning
|2 fast
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|a Neural networks (Computer science)
|2 fast
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|a Electronic books.
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|a proceedings (reports)
|2 aat
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|a Conference papers and proceedings
|2 fast
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|a Conference papers and proceedings.
|2 lcgft
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|a Actes de congrès.
|2 rvmgf
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| 700 |
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|a Pimenidis, Elias.
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| 700 |
1 |
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|a Angelov, Plamen P.
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| 700 |
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|a Jayne, Chrisina.
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| 700 |
1 |
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|a Papaleonidas, Antonios.
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| 700 |
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|a Aydin, M. E.
|q (Mehmet E.)
|1 https://id.oclc.org/worldcat/entity/E39PCjvkydt8fR9tyyWKgqg9pd
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| 776 |
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|i Print version:
|a Pimenidis, Elias.
|t Artificial Neural Networks and Machine Learning - ICANN 2022.
|d Cham : Springer International Publishing AG, ©2022
|z 9783031159183
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| 830 |
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|a Lecture notes in computer science ;
|v 13529.
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| 856 |
4 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-15919-0
|y Click for online access
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|a SPRING-COMP2022
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| 994 |
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|a 92
|b HCD
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