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 ...
目前看来,设计高效率、高精度的DSC变体模块仍然是非常热点的研究方向之一②。 计算量比值 参考内容如下: ①: 深度可分离卷积 ②:卷积神经网络之深度可分离卷积(Depthwise Separable Convolution) 编辑于 2024-03-13 11:14・IP 属地天津 神经网络 深度学习(Deep Learning) 卷积...
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...
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，...
Depthwise separable convolution (DSC) was proposed to reduce computation especially in convolutional layers by separating one convolution into a spatial convolution and a pointwise convolution. In this paper, we apply DSC to the YOLO network for object detection and propose a faster version of DSC, ...
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...
dimensional data, making them well-suited for handling the intricate spatial-spectral relationships in hyperspectral images.This study presents a hybrid approach for hyperspectral image classification, combining 3D Depthwise Separable Convolution (3D DSC) and Depthwise Squeeze-and-Excitation Network (DSENet)...
Depthwise separable convolution (DSC) has become one of the essential structures for lightweight convolutional neural networks. Nevertheless, its hardware architecture has not received much attention. Several previous hardware designs incur either high off-chip memory traffic or large on-chip memory usage...
计算量比较: plainConv:DSC=2700:975 总结 由上可知,DSC不管在参数量还是计算效率上,都远远优于普通卷积。DSC作为普通卷积的一种替代品,它最大的优点就是计算效率非常高,因此使用DSC构建轻量化模型是当下非常常见的做法,不过DSC的这种高效率是以牺牲精度为代价的。在今后用DSC构建轻量化模型过程中,需要全方位平衡...
fda dsc semantic-segmentation domain-adaptation depthwise-separable-convolutions self-supervised-learning unsupervised-domain-adaptation fourier-domain-adaptaion Updated Jul 26, 2024 Python AhmedERady / Grad_Project Star 1 Code Issues Pull requests Smart Automation Controller for Precision Agriculture ...