CNN for Computer Vision tasks
Object is a neural networks in the field of computer vision and data analysis. The article explores the fundamental principles and aspects underlying the functioning of neural networks. Among the identified limitations are the complexity of tuning hyperparameters and computational costs associated...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Igor Sikorsky Kyiv Polytechnic Institute
2024-03-01
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| Series: | Adaptivni Sistemi Avtomatičnogo Upravlinnâ |
| Subjects: | |
| Online Access: | https://asac.kpi.ua/article/view/302422 |
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| _version_ | 1849424470218899456 |
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| author | Р. Ковальчук О. Польшакова |
| author_facet | Р. Ковальчук О. Польшакова |
| author_sort | Р. Ковальчук |
| collection | DOAJ |
| description |
Object is a neural networks in the field of computer vision and data analysis. The article explores the fundamental principles and aspects underlying the functioning of neural networks. Among the identified limitations are the complexity of tuning hyperparameters and computational costs associated with increasing the network's depth and the size of training data. The aim of the study is to analyze contemporary solutions related to convolutional neural networks (CNN) to select an optimal topology that enhances addressing typical computer vision tasks. The article covers essential methods such as convolutional, pooling, fully connected, and inception layers. Different types of convolutional operations (expanded, partial, strided) and various CNN models (LeNet, AlexNet, VGGNet, GoogLeNet, ResNet) are also examined. After reaching the goal or objectives, it is suggested to employ various methods such as dimensionality reduction, batch normalization, data augmentation, utilization of advanced optimization methods, diverse activation functions, as well as combinations of these approaches.
Ref. 10, pic. 4, tabl. 1
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| format | Article |
| id | doaj-art-608e4bf879ab4180a2d4154dad089fc4 |
| institution | Kabale University |
| issn | 1560-8956 2522-9575 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Igor Sikorsky Kyiv Polytechnic Institute |
| record_format | Article |
| series | Adaptivni Sistemi Avtomatičnogo Upravlinnâ |
| spelling | doaj-art-608e4bf879ab4180a2d4154dad089fc42025-08-20T03:30:09ZengIgor Sikorsky Kyiv Polytechnic InstituteAdaptivni Sistemi Avtomatičnogo Upravlinnâ1560-89562522-95752024-03-0114410.20535/1560-8956.44.2024.302422340793CNN for Computer Vision tasksР. Ковальчук0О. Польшакова1Igor Sikorsky Kyiv Polytechnic InstituteIgor Sikorsky Kyiv Polytechnic Institute Object is a neural networks in the field of computer vision and data analysis. The article explores the fundamental principles and aspects underlying the functioning of neural networks. Among the identified limitations are the complexity of tuning hyperparameters and computational costs associated with increasing the network's depth and the size of training data. The aim of the study is to analyze contemporary solutions related to convolutional neural networks (CNN) to select an optimal topology that enhances addressing typical computer vision tasks. The article covers essential methods such as convolutional, pooling, fully connected, and inception layers. Different types of convolutional operations (expanded, partial, strided) and various CNN models (LeNet, AlexNet, VGGNet, GoogLeNet, ResNet) are also examined. After reaching the goal or objectives, it is suggested to employ various methods such as dimensionality reduction, batch normalization, data augmentation, utilization of advanced optimization methods, diverse activation functions, as well as combinations of these approaches. Ref. 10, pic. 4, tabl. 1 https://asac.kpi.ua/article/view/302422artificial intelligencecomputer visionclassificationdeep learningneural networksconvolutional neural networks |
| spellingShingle | Р. Ковальчук О. Польшакова CNN for Computer Vision tasks Adaptivni Sistemi Avtomatičnogo Upravlinnâ artificial intelligence computer vision classification deep learning neural networks convolutional neural networks |
| title | CNN for Computer Vision tasks |
| title_full | CNN for Computer Vision tasks |
| title_fullStr | CNN for Computer Vision tasks |
| title_full_unstemmed | CNN for Computer Vision tasks |
| title_short | CNN for Computer Vision tasks |
| title_sort | cnn for computer vision tasks |
| topic | artificial intelligence computer vision classification deep learning neural networks convolutional neural networks |
| url | https://asac.kpi.ua/article/view/302422 |
| work_keys_str_mv | AT rkovalʹčuk cnnforcomputervisiontasks AT opolʹšakova cnnforcomputervisiontasks |