Publications tagged with Convolution
Published:
Publications tagged with "Convolution"
- DetailsNapoli, F., Castaldo, M., Marrone, S., & Campanile, L. (2026). Comparing Emerging Technologies in Image Classification: From Quantum to Kolmogorov [Conference paper]. Lecture Notes in Computer Science, 15886 LNCS, 260–273. https://doi.org/10.1007/978-3-031-97576-9_17
Abstract
The rapid evolution of Artificial Intelligence has led to significant advancements in image classification, with novel approaches emerging beyond traditional deep learning paradigms. This paper presents a comparative analysis of three distinct methodologies for image classification: classical Convolutional Neural Networks (CNNs), Kolmogorov-Arnold Networks (KANs) and KAN-based CNNs and Quantum Machine Learning using Quantum Convolutional Neural Networks. The study evaluates these models on the Labeled Faces in the Wild dataset, implementing the different classifiers with existing, well-assessed technologies. Given the fundamental differences in computational paradigms, performance assessment extends beyond traditional accuracy metrics to include computational efficiency, interpretability, and, for quantum models, gate depth and noise. As a summary of the results, the proposed Quantum Convolutional Neural Network (QCNN) model achieves an accuracy of 75% on the target images classification task, indicating promising performance within current quantum computational limits. All the experiments strongly suggest that Convolutional Kolmogorov-Arnold Networks (CKANs) exhibit increased accuracy as image resolution decreases, QCNN performance meaningfully changes in relation to noise level, while CNNs still keeping strong discriminative capabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. - DetailsNapoli, F., Campanile, L., De Gregorio, G., & Marrone, S. (2025). Quantum Convolutional Neural Networks for Image Classification: Perspectives and Challenges [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 509–516. https://doi.org/10.5220/0013521500003944
Abstract
Quantum Computing is becoming a central point of discussion in both academic and industrial communities. Quantum Machine Learning is one of the most promising subfields of this technology, in particular for image classification. In this paper, the model of Quantum Convolutional Neural Networks and some related implementations are explored in their potential for a non-trivial task of image classification. The paper presents some experimentations and discusses the limitations and the strengths of these approaches when compared with classical Convolutional Neural Networks. Furthermore, an analysis of the impact of the noise level on the quality of the classification task has been performed. This paper reports a substantial equivalence of the perfomance of the model with respect the level of noise. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.