Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. So far, these neural networks (NNs) have mostly been developed for regular Euclidean domains such as those supporting images, audio, or video. Because...
Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been developed for regular Euclidean domains such as those supporting images, audio, or video. Because of their success, CNN...
Convolutional neural networks for melt depth prediction and visualization in laser powder bed fusion Powder bed fusion is a method of additive manufacturing (AM) where parts are constructed by iteratively melting metal cross-sections to build complex 3D st... F Ogoke,W Lee,NY Kao,... - 《Int...
Spherical Convolutional NeuralNetworks for Survival RatePrediction in Cancer PatientsFabian Sinzinger 1 * , Mehdi Astaraki 1,2 , Örjan Smedby 1 and Rodrigo Moreno 11 Division of Biomedical Imaging, Department of Biomedical Engineering and Health Systems, KTH Royal Institute ofTechnology, Stockholm, ...
Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and...
DeepSphere: a spherical convolutional neural network Janis Fluri,Nathanaël Perraudin,Michaël Defferrard This is an implementation of DeepSphere using TensorFlow 2.x. Resources Code: deepsphere-cosmo-tf1: original repository, implemented in TensorFlow v1. ...
In this work, we present SphereNet, a novel deep learning framework which encodes invariance against such distortions explicitly into convolutional neural networks. Towards this goal, SphereNet adapts the sampling locations of the convolutional filters, effectively reversing distortions, and wraps the ...
point clouds. The effectiveness of theproposed technique is established on the benchmark tasksof 3D object classif i cation and segmentation, achieving newstate-of-the-art on ShapeNet and RueMonge2014 datasets.1. IntroductionConvolutional Neural Networks (CNNs) [17] are knownto learn highly effecti...
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation J. Chang et al. Pyramid Stereo Matching Network J. Pang et al. Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching F.E. Wang et al. BiFuse: Monocular 360...
Convolutional neural networks (CNNs) and vision transformers (ViT), two examples of deep learning models for computer vision, analyze signals by assuming planar (flat) regions. Digital photographs, for example, are presented as a grid of pixels on a flat surface. ...