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Pansharpening by Convolutional Neural Networks [PDF]
Remote Sensing, 2016 A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem.
Giuseppe Masi +3 more
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Convolutional Neural Networks: A Survey
Computers, 2023 Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including ...
Moez Krichen
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Contextual Convolutional Neural Networks [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the standard convolution, which is the core component of convolutional neural networks. CoConv is implicitly equipped with the capability of incorporating contextual information while maintaining a similar number of parameters and computational cost ...
Ionut Cosmin Duta +2 more
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Hybrid quantum-classical-quantum convolutional neural networks [PDF]
Scientific ReportsDeep learning has achieved significant success in pattern recognition, with convolutional neural networks (CNNs) serving as a foundational architecture for extracting spatial features from images.
Changzhou Long +4 more
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A noise robust convolutional neural network for image classification
Results in Engineering, 2021 Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks.
Mohammad Momeny +4 more
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Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way
Sensors, 2023 Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a ...
Yaze Yu +4 more
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Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network
Inventions, 2021 In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications ...
Elena Solovyeva, Ali Abdullah
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Simplicial Convolutional Neural Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction.
Maosheng Yang, Elvin Isufi, Geert Leus
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INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks.
R. Niessner, H. Schilling, B. Jutzi
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Simplified Routing Mechanism for Capsule Networks
Algorithms, 2023 Classifying digital images using neural networks is one of the most fundamental tasks within the field of artificial intelligence. For a long time, convolutional neural networks have proven to be the most efficient solution for processing visual data ...
János Hollósi +2 more
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