Modern Convolutional Neural Networks⚓︎
:label:chap_modern_cnn
Now that we understand the basics of wiring together CNNs, let's take
a tour of modern CNN architectures. This tour is, by
necessity, incomplete, thanks to the plethora of exciting new designs
being added. Their importance derives from the fact that not only can
they be used directly for vision tasks, but they also serve as basic
feature generators for more advanced tasks such as tracking
:cite:Zhang.Sun.Jiang.ea.2021, segmentation :cite:Long.Shelhamer.Darrell.2015, object
detection :cite:Redmon.Farhadi.2018, or style transformation
:cite:Gatys.Ecker.Bethge.2016. In this chapter, most sections
correspond to a significant CNN architecture that was at some point
(or currently) the base model upon which many research projects and
deployed systems were built. Each of these networks was briefly a
dominant architecture and many were winners or runners-up in the
ImageNet competition
which has served as a barometer of progress on supervised learning in
computer vision since 2010. It is only recently that Transformers have begun
to displace CNNs, starting with :citet:Dosovitskiy.Beyer.Kolesnikov.ea.2021 and
followed by the Swin Transformer :cite:liu2021swin. We will cover this development later
in :numref:chap_attention-and-transformers.
While the idea of deep neural networks is quite simple (stack together a bunch of layers), performance can vary wildly across architectures and hyperparameter choices. The neural networks described in this chapter are the product of intuition, a few mathematical insights, and a lot of trial and error. We present these models in chronological order, partly to convey a sense of the history so that you can form your own intuitions about where the field is heading and perhaps develop your own architectures. For instance, batch normalization and residual connections described in this chapter have offered two popular ideas for training and designing deep models, both of which have since also been applied to architectures beyond computer vision.
We begin our tour of modern CNNs with AlexNet :cite:Krizhevsky.Sutskever.Hinton.2012,
the first large-scale network deployed to beat conventional computer
vision methods on a large-scale vision challenge; the VGG network
:cite:Simonyan.Zisserman.2014, which makes use of a number of
repeating blocks of elements; the network in network (NiN) that
convolves whole neural networks patch-wise over inputs
:cite:Lin.Chen.Yan.2013; GoogLeNet that uses networks with
multi-branch convolutions :cite:Szegedy.Liu.Jia.ea.2015; the residual
network (ResNet) :cite:He.Zhang.Ren.ea.2016, which remains one of
the most popular off-the-shelf architectures in computer vision;
ResNeXt blocks :cite:Xie.Girshick.Dollar.ea.2017
for sparser connections;
and DenseNet
:cite:Huang.Liu.Van-Der-Maaten.ea.2017 for a generalization of the
residual architecture. Over time many special optimizations for efficient
networks have been developed, such as coordinate shifts (ShiftNet) :cite:wu2018shift. This
culminated in the automatic search for efficient architectures such as
MobileNet v3 :cite:Howard.Sandler.Chu.ea.2019. It also includes the
semi-automatic design exploration of :citet:Radosavovic.Kosaraju.Girshick.ea.2020
that led to the RegNetX/Y which we will discuss later in this chapter.
The work is instructive insofar as it offers a path for marrying brute force computation with
the ingenuity of an experimenter in the search for efficient design spaces. Of note is
also the work of :citet:liu2022convnet as it shows that training techniques (e.g., optimizers, data augmentation, and regularization)
play a pivotal role in improving accuracy. It also shows that long-held assumptions, such as
the size of a convolution window, may need to be revisited, given the increase in
computation and data. We will cover this and many more questions in due course throughout this chapter.
:maxdepth: 2
alexnet
vgg
nin
googlenet
batch-norm
resnet
densenet
cnn-design
创建日期: November 25, 2023