A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep...

certain existing neural network architectures can be interpreted as GNNs operating on suitably defined graphs. A convolutional neural network layer, in the...

networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance...

artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are...

recurrent neural networks and convolutional neural networks, renewed interest in ANNs. The 2010s saw the development of a deep neural network (i.e., one...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia...

Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights...

LeNet is a series of convolutional neural network architectures created by a research group in AT&T Bell Laboratories during the 1988 to 1998 period, centered...

and 2) model context at each layer of the network. It is essentially a 1-d convolutional neural network (CNN). Shift-invariant classification means...

convolutional neural network best performance was 0.25 percent error rate. As of August 2018, the best performance of a single convolutional neural network...

Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and...

closely mimic biological neural organization. The idea is to add structures called "capsules" to a convolutional neural network (CNN), and to reuse output...

AlexNet is a convolutional neural network architecture developed for image classification tasks, notably achieving prominence through its performance in...

The VGGNets are a series of convolutional neural networks (CNNs) developed by the Visual Geometry Group (VGG) at the University of Oxford. The VGG family...

is a series of real-time object detection systems based on convolutional neural networks. First introduced by Joseph Redmon et al. in 2015, YOLO has...

called "positive part") was critical for object recognition in convolutional neural networks (CNNs), specifically because it allows average pooling without...

Alex Krizhevsky and Geoffrey Hinton, he co-invented AlexNet, a convolutional neural network. Sutskever co-founded and was a former chief scientist at OpenAI...

types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate...

U-Net is a convolutional neural network that was developed for image segmentation. The network is based on a fully convolutional neural network whose architecture...

An optical neural network is a physical implementation of an artificial neural network with optical components. Early optical neural networks used a photorefractive...

of a Convolutional Neural Network". Neurocomputing. 407: 439–453. doi:10.1016/j.neucom.2020.04.018. S2CID 219470398. Convolutional neural networks represent...

A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions...

MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision...

generator is typically a deconvolutional neural network, and the discriminator is a convolutional neural network. GANs are implicit generative models, which...

A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on...

of the two objective functions. An approach that integrates a convolutional neural network has been proposed and shows better results (albeit with a slower...

type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly...

In the VC-6 standard an up-sampler developed with an in-loop Convolutional Neural Network is provided to optimize the detail in the reconstructed image...

Generators (QRNGs) to machine learning models including Neural Networks and Convolutional Neural Networks for random initial weight distribution and Random...