The present disclosure provides systems and methods that apply neural networks such as, for example, convolutional neural networks, to sparse imagery in an improved manner. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous ...
Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of high-resolution shapes represented as binary voxel occupancy grids. In contr...
SparseConvolutionalNeuralNetworks 系统标签: convolutionalsparseneuralnetworkskernelssparsity SparseConvolutionalNeuralNetworks∗BaoyuanLiu1,MinWang1,HassanForoosh1,MarshallTappen3,andMariannaPenksy21ComputationalImagingLab,ComputerScience,UniversityofCentralFlorida,Orlando,FL,USA2DepartmentofMathematics,UniversityofCentral...
Convolutional Neural Networks(2):Sparse Interactions, Receptive Field and Parameter Sharing Sparse Interactions, Receptive Field and Parameter Sharing是整个CNN深度网络的核心部分,我们用本文来具体分析其原理。 首先我们考虑Feedforward Neural Network,L层的输出矩阵,等于L层的输入矩阵与L层的权重矩阵做矩阵乘法,而...
Sparse3DconvolutionalneuralnetworksBenGrahamUniversityofWarwickb.graham@warwick.ac.ukMay12,2015AbstractWehaveimplementedaconvolutionalneural..
Convolutional neural networkSparse matrix multiplicationIn this paper, we propose a block-sparse convolutional neural network (BSCNN) architecture that converts a dense convolution kernel into a sparse one. Traditional convolutional neural networks (CNNs) face the problem that an increase in the number...
An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs Abstract 深度卷积神经网络(CNN)在广泛的应用中都实现了最先进的性能。但是,复杂的人工智能(AI)任务广泛需要更复杂的更深的CNN模型,这些模型通常对算力的要求极高。尽管最近在网络压缩方面的研究进展(例如剪枝)已经显著减轻...
Introduced by Graham in Spatially-sparse convolutional neural networks Edit Source: Spatially-sparse convolutional neural networks Read Paper See Code Papers PaperCodeResultsDateStarsTasks Semantic SegmentationSemantic SegmentationObject DetectionObject Detection3D Object Detection3D Object Detection...
Penksy. Sparse Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion, 2015. [14] J. Long, E. Shelhamer, and T. Darrell. Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and ...
ReLU——Deep Sparse Rectifier Neural Networks 1. 摘要 ReLU 相比 Tanh 能产生相同或者更好的性能,而且能产生真零的稀疏表示,非常适合自然就稀疏的数据。 采用ReLU 后,在大量的有标签数据下,有没有无监督预训练模型取得的最好效果是一样的,这可以被看做是训练深层有监督网络的一个新的里程碑。