Convolutional Neural Networks: Transforming Visual Intelligence Through Hierarchical Feature Learning

Convolutional Neural Networks: Transforming Visual Intelligence Through Hierarchical Feature Learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 7 October 2025 V1 Latest version Share on Convolutional Neural Networks: Transforming Visual Intelligence Through Hierarchical Feature Learning Authors : Surya Rao Rayarao 0009-0001-8467-7865 [email protected] and Naga Donikena Authors Info & Affiliations https://doi.org/10.22541/au.175986267.72682330/v1 252 views 235 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision and pattern recognition, establishing themselves as the dominant architecture for processing grid-structured data such as images and videos. This paper provides a comprehensive review of CNN architectures, examining their fundamental mechanisms, operational principles, and distinguishing characteristics that set them apart from traditional neural networks. We explore the hierarchical feature learning capabilities of CNNs through convolutional layers, pooling operations, and fully connected layers, demonstrating how these components work synergistically to extract meaningful representations from raw visual data. Furthermore, we discuss the substantial advantages CNNs offer over conventional neural network architectures, including translation invariance, parameter sharing, and spatial hierarchy preservation. The paper surveys key applications across diverse domains including medical imaging, autonomous vehicles, natural language processing, and industrial automation, showcasing the transformative impact of CNNs on modern artificial intelligence systems. Through detailed examination of architectural innovations and practical implementations, this work serves as a comprehensive resource for understanding the principles and applications of convolutional neural networks. Supplementary Material File (2025_10_cnn.pdf) Download 488.28 KB Information & Authors Information Version history V1 Version 1 07 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords computer vision convolutional neural networks deep learning feature learning image classification neural network architectures pattern recognition Authors Affiliations Surya Rao Rayarao 0009-0001-8467-7865 [email protected] Department of Statistics and Data Sciences Department of Computer Science, The University of Texas at Austin Austin View all articles by this author Naga Donikena Department of Statistics and Data Sciences Department of Computer Science, The University of Texas at Austin Austin View all articles by this author Metrics & Citations Metrics Article Usage 252 views 235 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Surya Rao Rayarao, Naga Donikena. Convolutional Neural Networks: Transforming Visual Intelligence Through Hierarchical Feature Learning. Authorea . 07 October 2025. DOI: https://doi.org/10.22541/au.175986267.72682330/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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