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

earlier neural networks. To speed processing, standard convolutional layers can be replaced by depthwise separable convolutional layers, which are based on...

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

introduced in 2016, Twin fully convolutional network has been used in many High-performance Real-time Object Tracking Neural Networks. Like CFnet, StructSiam...

S2CID 206775608. LeCun, Yann. "LeNet-5, convolutional neural networks". Retrieved 16 November 2013. "Convolutional Neural Networks (LeNet) – DeepLearning 0.1 documentation"...

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...

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...

trained 6 experts, each being a "time-delayed neural network" (essentially a multilayered convolution network over the mel spectrogram). They found that...

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

field of neurons in later layers in the network. Pooling is most commonly used in convolutional neural networks (CNN). Below is a description of pooling...

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...

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

on optical character recognition and computer vision using convolutional neural networks (CNNs). He is also one of the main creators of the DjVu image...

A neural radiance field (NeRF) is a neural field for reconstructing a three-dimensional representation of a scene from two-dimensional images. The NeRF...

RCNN may refer to: Region Based Convolutional Neural Networks, a family of machine learning models for computer vision and specifically object detection...

memory neural networks Bayesian networks Hidden Markov models (HMMs) Minimum Covariance Determinant Deep Learning Convolutional Neural Networks (CNNs):...

models that rely on convolutional neural networks with the aim of improving early diagnostic accuracy. Generative adversarial networks are a form of deep...

feedforward neural networks (i.e., Multi-Layer Perceptrons (MLPs)), (2) convolutional neural networks (CNNs), and (3) recurrent neural networks (RNNs). Recently...

by HMMs. Convolutional neural networks (CNN) are a class of deep neural network whose architecture is based on shared weights of convolution kernels or...

minor intensity variations in input patterns, etc. In 2015, convolutional neural networks reached state of the art in semantic segmentation. U-Net is...

as a leading neural networks architecture at the Princeton ModelNet competition. It is a fusion of three convolutional neural networks, one trained on...

(SSD) Single-Shot Refinement Neural Network for Object Detection (RefineDet) Retina-Net Deformable convolutional networks Feature detection (computer vision)...

species. A large amount of research in this area has been focused on the neural basis of human intelligence. Historic approaches to studying the neuroscience...

for reinforcement learning in neural networks". Proceedings of the IEEE First International Conference on Neural Networks. CiteSeerX 10.1.1.129.8871. Peters...

the distance between the symbols and the wedges. The Region Based Convolutional Neural Network was trained on 3D models of 1,977 cuneiform tablets, with...

used for training a neural network in computing parameter updates. It is an efficient application of the chain rule to neural networks. Backpropagation computes...

to many modalities through the use of deep neural network architectures such as convolutional neural networks and transformers. Supervised feature learning...

methods and convolutional neural networks can be used to acquire an embedding of images bound to a given geographical object or a region. A single point...

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns...

helix transform computes the multidimensional convolution by incorporating one-dimensional convolutional properties and operators. Instead of using the...