深度卷积网络Inception：ILSVRC2014新纪录

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"Going Deeper with Convolutions" 论文介绍了Inception网络架构，这是一种深度卷积神经网络，旨在提高图像识别与检测任务的性能，并在2014年ImageNet Large-Scale Visual Recognition Challenge (ILSVRC14)中达到最新水平。在论文中，作者Christian Szegedy等人提出的主要观点是通过改进网络设计，更有效地利用计算资源，增加网络的深度和宽度，同时保持计算成本不变。他们设计了一种称为Inception的架构，其特点在于多尺度处理的直觉和基于赫布原则的结构优化。Inception网络通过并行的卷积层、池化层以及不同大小的滤波器，实现对不同尺度特征的捕获，从而提高了模型的表达能力。具体来说，Inception模块是网络的核心，它包含了1x1、3x3、5x5的卷积层以及一个最大池化层，这些组件并行连接。1x1卷积用于减少输入通道的维度，降低计算复杂度，而3x3和5x5卷积则能捕捉更大范围的上下文信息。最大池化层则提供了对局部特征的保留。通过这种方式，Inception架构能够在不增加计算负担的情况下增加网络的复杂性。此外，论文还介绍了一个特定的Inception变体——GoogLeNet，这是一个包含22层的深度网络。GoogLeNet在ILSVRC14上的表现证明了Inception架构的有效性，不仅在分类任务上取得优秀成绩，而且在目标检测任务中也有突出表现。 “Going Deeper with Convolutions”这篇论文提出了Inception网络，这是深度学习领域的一个重要里程碑，它推动了卷积神经网络的设计和发展，为后续的深度学习模型如ResNet、 DenseNet等奠定了基础。通过创新的网络设计，Inception网络显著提高了图像识别的准确性和效率，对于理解和优化深度学习模型具有重要意义。

Going Deeper with Convolutions

Christian Szegedy

, Wei Liu

, Yangqing Jia

, Pierre Sermanet

, Scott Reed

Dragomir Anguelov

, Dumitru Erhan

, Vincent Vanhoucke

, Andrew Rabinovich

Google Inc.

University of North Carolina, Chapel Hill

University of Michigan, Ann Arbor

Magic Leap Inc.

{szegedy,jiayq,sermanet,dragomir,dumitru,vanhoucke}@google.com

wliu@cs.unc.edu,

reedscott@umich.edu,

arabinovich@magicleap.com

Abstract

We propose a deep convolutional neural network ar-

chitecture codenamed Inception that achieves the new

state of the art for classiﬁcation and detection in the Im-

ageNet Large-Scale Visual Recognition Challenge 2014

(ILSVRC14). The main hallmark of this architecture is the

improved utilization of the computing resources inside the

network. By a carefully crafted design, we increased the

depth and width of the network while keeping the compu-

tational budget constant. To optimize quality, the architec-

tural decisions were based on the Hebbian principle and

the intuition of multi-scale processing. One particular in-

carnation used in our submission for ILSVRC14 is called

GoogLeNet, a 22 layers deep network, the quality of which

is assessed in the context of classiﬁcation and detection.

1. Introduction

In the last three years, our object classiﬁcation and de-

tection capabilities have dramatically improved due to ad-

vances in deep learning and convolutional networks [10].

One encouraging news is that most of this progress is not

just the result of more powerful hardware, larger datasets

and bigger models, but mainly a consequence of new ideas,

algorithms and improved network architectures. No new

data sources were used, for example, by the top entries

in the ILSVRC 2014 competition besides the classiﬁcation

dataset of the same competition for detection purposes. Our

GoogLeNet submission to ILSVRC 2014 actually uses 12

times fewer parameters than the winning architecture of

Krizhevsky et al [9] from two years ago, while being sig-

niﬁcantly more accurate. On the object detection front, the

biggest gains have not come from naive application of big-

ger and bigger deep networks, but from the synergy of deep

architectures and classical computer vision, like the R-CNN

algorithm by Girshick et al [6].

Another notable factor is that with the ongoing traction

of mobile and embedded computing, the efﬁciency of our

algorithms – especially their power and memory use – gains

importance. It is noteworthy that the considerations leading

to the design of the deep architecture presented in this paper

included this factor rather than having a sheer ﬁxation on

accuracy numbers. For most of the experiments, the models

were designed to keep a computational budget of 1.5 billion

multiply-adds at inference time, so that the they do not end

up to be a purely academic curiosity, but could be put to real

world use, even on large datasets, at a reasonable cost.

In this paper, we will focus on an efﬁcient deep neural

network architecture for computer vision, codenamed In-

ception, which derives its name from the Network in net-

work 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 that we introduce a new level of organization

in the form of the “Inception module” and also in the more

direct sense of increased network depth. In general, one can

view the Inception model as a logical culmination of [12]

while taking inspiration and guidance from the theoretical

work by Arora et al [2]. The beneﬁts of the architecture are

experimentally veriﬁed on the ILSVRC 2014 classiﬁcation

and detection challenges, where it signiﬁcantly outperforms

the current state of the art.

2. Related Work

Starting with LeNet-5 [10], convolutional neural net-

works (CNN) have typically had a standard structure –

stacked convolutional layers (optionally followed by con-

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