深度学习Inception网络：ILSVRC2014新纪录

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"这篇学术论文名为《Going Deeper with Convolutions》，由Google Inc.的研究人员提出，他们在2014年的ImageNet Large-Scale Visual Recognition Challenge (ILSVRC14)中取得了分类和检测的新纪录。论文的核心是介绍了一种名为Inception的深度卷积神经网络架构，该架构通过精心设计提高了网络内部计算资源的利用率，同时增加了网络的深度和宽度，而计算成本保持不变。Inception网络的设计灵感来源于海布原理和多尺度处理的直觉，并在ILSVRC14比赛中被命名为GoogLeNet，这是一个包含22层的深度网络。" 在这篇论文中，作者们深入探讨了如何通过优化深度学习模型结构来提升计算机视觉任务的性能。他们提出的关键创新点是Inception架构，该架构旨在解决传统深度卷积网络中计算效率低下的问题。Inception网络的特点在于其模块化设计，每个模块包含了不同大小的卷积核，可以同时处理不同尺度的特征，这在一定程度上模拟了生物视觉系统的工作机制，遵循了海布学习规则。传统的卷积神经网络往往在增加层数时会带来计算量的显著增长，而Inception架构则通过巧妙的设计，如1x1卷积层，来减少通道间的计算复杂性，同时保持网络的表达能力。这种设计使得网络可以在不增加计算负担的情况下变得更深更宽，从而能够捕捉到更复杂的图像特征。此外，Inception架构还引入了池化操作的改进，例如使用平均池化和最大池化的结合，以保留更多信息并降低空间维度。在GoogLeNet的22层结构中，这些组件被组合在一起，形成了一种高效的特征提取和分类的深层网络。 ILSVRC14比赛的结果验证了Inception架构的有效性。GoogLeNet在图像分类和检测任务上的表现超越了当时的其他方法，标志着深度学习在计算机视觉领域的重大进步。这篇论文不仅推动了深度学习模型的设计理念，也为后续的ResNet、DenseNet等深度网络的发展奠定了基础。这篇论文的核心贡献在于引入了Inception架构，它通过多尺度处理和资源优化提升了深度学习模型的性能，为后续的深度学习研究提供了重要的理论和技术支持。

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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