深度可分离卷积(Depthwise Separable Convolution,DSC)最早出现在一篇名为“Rigid-motion scattering for image classification”的博士学位论文中。但让大家对DSC熟知的则是两个著名的模型,Xception[1]和MobileNet[2]。Xception和MobileNet是同一时期出自Google团队的两个重要成果。DSC的详细结构如图1.1所示。DSC由Depthwise C...
通过该操作,相同的输入,Depthwise Separable Convolution的参数个数是常规卷积的约1/N+1/D2K, 计算量可以大大减少,提高性能。因此,在参数的数量相同的前提下,采用Depthwise Separable Convolution的神经网络层数可以做的更深。 一些研究从增加网络整体非线性表达能力的角度出发,认为在Depthwise Convolution之后加入激活函数...
深度可分离卷积（Depthwise Separable Convolution，DSC）是卷积神经网络中一种高效的替代方案，尤其适用于移动设备等资源有限的场景。标准卷积中，每个卷积核对输入的所有通道进行操作，参数数量较多且计算成本高。相比之下，DSC分为两步：首先，深度卷积（Depthwise Convolution）通过单个卷积核处理每个输入通道，...
在卷积神经网络的世界里，深度可分离卷积(Depthwise Separable Convolution, DSC)作为一种高效的架构革新，正崭露头角。它由两个关键部分组成：Depthwise Convolution和Pointwise Convolution，为资源受限的场景提供了强大的计算效率提升。本文将深入探讨DSC的起源、结构以及其在Xception、MobileNet和ResNet中的应用...
Depthwise Separable Convolution is a network architecture technique that involves breaking down a convolution operation into two parts: depthwise convolution, which operates on individual input channels, and pointwise convolution, which increases the dimension of the feature map by combining information from...
Depthwise separable convolutions (DSC) have been widely deployed in lightweight convolutional neural networks due to high efficiency. But the acceleration performance of the Graphics Processing Unit for DSC was not as well as in theory. In this paper, some approaches were proposed for accelerating ...
fdadscsemantic-segmentationdomain-adaptationdepthwise-separable-convolutionsself-supervised-learningunsupervised-domain-adaptationfourier-domain-adaptaion UpdatedJul 26, 2024 Python Neural Network for Low Complexity Acoustic Scene Classification pythonmachine-learningneural-networkdepthwise-separable-convolutionsdilated-...
Paper tables with annotated results for Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets
Depthwise separable convolution (DSC) has become one of the essential structures for lightweight convolutional neural networks. Nevertheless, its hardware ... B Li,H Wang,X Zhang,... - 《IEEE Transactions on Circuits & Systems I Regular Papers A Publication of the IEEE Circuits & Systems Societ...
而深度可分离卷积(Depthwise Separable Convolution,DSC)的提出正是用来减少网络参数量与计算量,来提高网络运行效率的。通过在网络中使用DSC,Google团队成功打造出移动端轻量化网络MobileNets[1],该网络在保证高精度的同时,极大减少了模型整体的参数量与计算量。