Results 31 to 40 of about 73,468 (309)
Comparing Incremental Learning Strategies for Convolutional Neural Networks [PDF]
, 2016 In the last decade, Convolutional Neural Networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful high-level invariant features.
LOMONACO, VINCENZO +3 more
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Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications
Frontiers in Neuroscience, 2020 In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice ...
Martino Sorbaro +4 more
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A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging
Engineering Proceedings, 2023 Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging.
Satyam Tiwari +5 more
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Self-organized operational neural networks for severe image restoration problems [PDF]
, 2020 Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs. It has become the go-to methodology for tackling image restoration and has outperformed
Malik, Junaid +2 more
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Lattice gauge equivariant convolutional neural networks [PDF]
, 2022 We propose Lattice gauge equivariant Convolutional Neural Networks (L-CNNs) for generic machine learning applications on lattice gauge theoretical problems.
Favoni, Matteo; orcid: +3 more
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Creating Deep Convolutional Neural Networks for Image Classification
The Programming Historian, 2023 This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google’s Teachable Machine to train a model on ...
Nabeel Siddiqui
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Remote Sensing, 2020 The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment.
Guojie Wang +3 more
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Quantum convolutional neural networks for high energy physics data analysis
Physical Review Research, 2022 This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment.
Samuel Yen-Chi Chen +4 more
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A Review of Convolutional Neural Network Development in Computer Vision
EAI Endorsed Transactions on Internet of Things, 2022 Convolutional neural networks have made admirable progress in computer vision. As a fast-growing computer field, CNNs are one of the classical and widely used network structures. The Internet of Things (IoT) has gotten a lot of attention in recent years.
Hang Zhang
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A robust deformed convolutional neural network (CNN) for image denoising
CAAI Transactions on Intelligence Technology, 2022 Abstract Due to strong learning ability, convolutional neural networks (CNNs) have been developed in image denoising. However, convolutional operations may change original distributions of noise in corrupted images, which may increase training difficulty in image denoising.
Qi Zhang 0059 +4 more
openaire +3 more sources