
MatConvNet
 Referenced in 16 articles
[sw15651]
 MatConvNet – convolutional neural networks for MATLAB. MatConvNet is an open source implementation of Convolutional Neural ... MATLAB functions, providing routines for computing convolutions with filter banks, feature pooling, normalisation, and much...

DeepLab
 Referenced in 17 articles
[sw15303]
 feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively ... multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates...

SegNet
 Referenced in 16 articles
[sw27575]
 novel and practical deep fully convolutional neural network architecture for semantic pixelwise segmentation termed ... engine consists of an encoder network, a corresponding decoder network followed by a pixelwise ... encoder network is topologically identical to the 13 convolutional layers in the VGG16 network...

PDENet
 Referenced in 28 articles
[sw36963]
 neural network designs in deep learning, we propose a new feedforward deep network, called ... differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning...

VNet
 Referenced in 5 articles
[sw35860]
 Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Convolutional Neural Networks (CNNs) have been ... segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained...

PTE
 Referenced in 6 articles
[sw37756]
 Text Embedding through Largescale Heterogeneous Text Networks. Unsupervised text embedding methods, such as Skip ... sophisticated deep learning architectures such as convolutional neural networks, these methods usually yield inferior results ... represented as a largescale heterogeneous text network, which is then embedded into ... recent supervised approaches based on convolutional neural networks, predictive text embedding is comparable or more...

CNNRNN
 Referenced in 8 articles
[sw28401]
 Multilabel Image Classification. While deep convolutional neural networks (CNNs) have shown a great success...

VoxNet
 Referenced in 5 articles
[sw36666]
 VoxNet: A 3D Convolutional Neural Network for realtime object recognition. Robust object recognition ... Grid representation with a supervised 3D Convolutional Neural Network (3D CNN). We evaluate our approach...

Lasagne
 Referenced in 6 articles
[sw20936]
 neural networks in Theano. Its main features are: Supports feedforward networks such as Convolutional...

DoReFaNet
 Referenced in 4 articles
[sw36246]
 DoReFaNet: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. We propose DoReFa ... method to train convolutional neural networks that have low bitwidth weights and activations using ... numbers before being propagated to convolutional layers. As convolutions during forward/backward passes can now operate ... accelerate training of low bitwidth neural network on these hardware. Our experiments on SVHN...

DeepTracker
 Referenced in 3 articles
[sw25889]
 DeepTracker: Visualizing the Training Process of Convolutional Neural Networks. Deep convolutional neural networks (CNNs) have...

SyncSpecCnn
 Referenced in 5 articles
[sw26163]
 vertex functions on them by convolutional neural networks, we resort to spectral CNN method that ... graph laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strive to overcome ... introduce a spectral parameterization of dilated convolutional kernels and a spectral transformer network. Experimentally...

MgNet
 Referenced in 3 articles
[sw35862]
 unified framework of multigrid and convolutional neural network. We develop a unified model, known ... MgNet, that simultaneously recovers some convolutional neural networks (CNN) for image classification and multigrid ... unified model, the function of various convolution operations and pooling used...

Conformer
 Referenced in 3 articles
[sw35794]
 Speech Recognition. Recently Transformer and Convolution neural network (CNN) based models have shown promising results ... Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing ... worlds by studying how to combine convolution neural networks and transformers to model both local ... this regard, we propose the convolutionaugmented transformer for speech recognition, named Conformer. Conformer significantly...

JigsawNet
 Referenced in 3 articles
[sw25887]
 JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loopbased Composition. This paper proposes ... complicated puzzles. We build a deep convolutional neural network to detect the compatibility...

Keras
 Referenced in 119 articles
[sw15491]
 Keras is a minimalist, highly modular neural networks library, written in Python and capable ... total modularity, minimalism, and extensibility). supports both convolutional networks and recurrent networks, as well...

CayleyNets
 Referenced in 4 articles
[sw38090]
 CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters. The rise of graphstructured ... such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with ... paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs...

SYNTHIA Dataset
 Referenced in 4 articles
[sw35060]
 driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable...

fpgaConvNet
 Referenced in 2 articles
[sw25907]
 fpgaConvNet: A framework for mapping Convolutional Neural Networks on FPGAs. Convolutional Neural Networks (ConvNets/CNNs...

Pixel2Mesh
 Referenced in 3 articles
[sw31205]
 Limited by the nature of deep neural network, previous methods usually represent a 3D shape ... mesh in a graphbased convolutional neural network and produces correct geometry by progressively deforming...