16 skills found
iesl / Dilated Cnn NerDilated CNNs for NER in TensorFlow
kristpapadopoulos / SeriesnetTime series prediction using dilated causal convolutional neural nets (temporal CNN)
USTCPCS / CVPR2018 AttentionContext Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark
dr-costas / Dnd SedSound event detection with depthwise separable and dilated convolutions.
nlpdz / Medical Named Entity Rec Based On Dilated CNN基于膨胀卷积神经网络（Dilated Convolutions）训练好的医疗命名实体识别工具
xiongma / DGCNNDilation Gate CNN For Machine Reading Comprehension
many-facedgod / Numpy Atrous Transposed CNNA Numpy implementation of the dilated/atrous CNNs proposed by Yu et al. as well as transposed convolutions.
XierHacker / ChineseWordSegmentTensorflow Implements Chinese Word Segment use LSTM+CRF and Dilated CNN+CRF
jingkunchen / MS CMR Miccai 2019Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual U-shape network is used as a segmentor to generate the prediction mask; meanwhile, a CNN is utilized as a discriminator model to judge the segmentation quality. To leverage the available annotations across modalities per patient, a new loss function named weak domain-transfer loss is introduced to the pipeline. The proposed model is evaluated on the public dataset released by the challenge organizer in MICCAI 2019, which consists of 45 sets of multi-sequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deep-learning methods.
ziyangwang007 / TriConvUNeXtExploring Dilated CNN, Deformable CNN, and Depthwise CNN for medical image segmentation.
nithish08 / Dilated CnnDilated & Causal Convolutions on time-series data | PyTorch | Keras
chenhuims / Time Series CnnTime series forecasting using convolutional neural networks (CNNs) including dilated CNNs
Prograf-UFF / ConformalLayersConformalLayers is a conformal embedding of sequential layers of Convolutional Neural Networks (CNNs) that allows associativity between operations like convolution, average pooling, dropout, flattening, padding, dilation, and stride. Such embedding allows associativity between layers of CNNs, considerably reducing the number of operations to perform inference in neural networks.
KamenDamov / IFT3710 Advanced Project In ML AIThe aim of the project will be to combine a CNN architecture with dilated convolutions and include them in GANs to generate biological cell segmentation. We will also compare this new architecture with state-of-the-art models, as well as with more rudimentary models.
yanmeen / AfnnNeural Network models for autofocus of microscopy, include Residual, Deep CNN, Dilated CNN models using MLP and Regression
kyegomez / CNNGPTThis CNN-based language model leverages causal and dilated convolutions, gated activations, residual connections, and layer normalization to effectively model textual data for generation tasks.
