Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]. computer vision and pattern recognition, 2015: 1-9. @article{szegedy2015going, titl
《GoogLeNet-Going deeper with convolutions》是Google公司Inception系列的开山之作,在这篇文章中首次提出了Inception模块,后面的Inception v2&v3、Inception v4也是在这篇文章的基础上改进的。GoogLeNet是2014年ILSVRC挑战赛冠军,将Top5 的错误率降低到6.67%,是一个22层的深度网络。代码实现将放在本文末尾。 前言 作者...
def __init__(self, in_channels, out_channels, **kwargs): super(BasicConv2d, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, **kwargs) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.conv(x) x = self.relu(x) return x googlenet = Go...
computer vision, codenamed Inception, which derives its name from the Network in network paper by Lin et al [12] in conjunction with the famous “we need to go deeper” internet meme [1]. In our case, the word “deep” is used in two different meanings: first of all, in the sense ...
代码连接:https://github.com/titu1994/Inception-v4(包含v1,v2,v4) 摘要 本文提出了一个深层的卷积网络结构-Inception,该结构的主要特点是提高了网络内部计算资源的利用率。在预估计算资源消耗量不变的情况下增加网络的深度及宽度。为了进行有效的优化,结构决策基于Hebbian原理及多尺寸处理操作。本文思想的一个经典...
Going Deeper with Convolutions 论文下载 https://volctracer.com/w/BX18q92F 论文作者 Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich 内容简介 这篇论文提出了一种深度卷积神经网络架构，代号为Inception（也称...
GoogLeNet: Going deeper with convolutions 1 前言 各位朋友大家好,欢迎来到月来客栈。在上一篇文章中,笔者花了很大的篇幅介绍完了GoogLeNet中的核心部分Inception模块。其本质上来说就是采用了不同尺度的卷积核对输入进行特征提取,然后再将各个部分得到的结果进行组合,最后在输入到下一层的网络中。
title={Going deeper with convolutions}, author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew}, pages={1--9}, ...
深度学习论文阅读(四):GoogLeNet《Going Deeper with Convolutions》 Abstract 摘要 1.Introduction 引言 2. Related Work 相关工作 3.Motivation and High Level Considerations 动机和高层思考 4.Architectural Details 架构细节 5.GoogLeNet 6.Training Methodology 训练方法 ...
Going Deeper with Convolutions Abstract We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture...