• AlexNet

  • Referenced in 343 articles [sw38522]
  • AlexNet is a convolutional neural network that is 8 layers deep. You can load...
  • DnCNN

  • Referenced in 26 articles [sw39678]
  • construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very...
  • MatConvNet

  • Referenced in 15 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...
  • SegNet

  • Referenced in 21 articles [sw27575]
  • novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed ... engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise ... encoder network is topologically identical to the 13 convolutional layers in the VGG16 network...
  • DeepLab

  • Referenced in 20 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...
  • XNOR-Net

  • Referenced in 11 articles [sw39593]
  • XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. We propose two efficient approximations ... convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters ... saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary...
  • MobileNets

  • Referenced in 12 articles [sw39590]
  • MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. We present a class of efficient ... depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple...
  • Xception

  • Referenced in 9 articles [sw39068]
  • interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between ... depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution ... propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have ... been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms...
  • PDE-Net

  • Referenced in 34 articles [sw36963]
  • neural network designs in deep learning, we propose a new feed-forward deep network, called ... differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning...
  • DoReFa-Net

  • Referenced in 6 articles [sw36246]
  • DoReFa-Net: 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...
  • CNN-RNN

  • Referenced in 9 articles [sw28401]
  • Multi-label Image Classification. While deep convolutional neural networks (CNNs) have shown a great success...
  • V-Net

  • 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...
  • VoxNet

  • Referenced in 6 articles [sw36666]
  • VoxNet: A 3D Convolutional Neural Network for real-time object recognition. Robust object recognition ... Grid representation with a supervised 3D Convolutional Neural Network (3D CNN). We evaluate our approach...
  • Lasagne

  • Referenced in 7 articles [sw20936]
  • neural networks in Theano. Its main features are: Supports feed-forward networks such as Convolutional...
  • ShuffleNet

  • Referenced in 8 articles [sw39585]
  • ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. We introduce an extremely computation ... architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation...
  • EfficientNet

  • Referenced in 4 articles [sw39587]
  • EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Convolutional Neural Networks (ConvNets) are commonly developed...
  • PTE

  • Referenced in 5 articles [sw37756]
  • Text Embedding through Large-scale 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 large-scale heterogeneous text network, which is then embedded into ... recent supervised approaches based on convolutional neural networks, predictive text embedding is comparable or more...
  • Keras

  • Referenced in 131 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...
  • 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...
  • MeshCNN

  • Referenced in 4 articles [sw31207]
  • inhibits mesh analysis efforts using neural networks that combine convolution and pooling operations. In this ... shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes. Analogous to classic...