Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

had 1.2 million images belonging to 1,000 categories, just like the 2012 edition.
VGG
The architecture developed by Karen Simonyan and Andrew Zisserman from the
Oxford Vision Geometry Group (VGG) is pretty much an even larger or, better yet,
deeper model than AlexNet (and now you know the origin of yet another
architecture name). Their goal is made crystal clear in their model’s description:
…we explore the effect of the convolutional network (ConvNet) depth on its
accuracy.
Source: Results (ILSVRC2014) [115]
VGG models are massive, so we’re not paying much attention to them here. If you
want to learn more about it, its paper is called "Very Deep Convolutional Networks
for Large-Scale Image Recognition." [116]
Inception (GoogLeNet Team)
The Inception architecture is probably the one with the best meme of all: "We need
to go deeper." The authors, Christian Szegedy, et al., like the VGG team, wanted to
train a deeper model. But they came up with a clever way of doing it (highlights are
mine):
Additional dimension reduction layers based on embedding learning
intuition allow us to increase both the depth and the width of the network
significantly without incurring significant computational overhead.
Source: Results (ILSVRC2014) [117]
If you want to learn more about it, the paper is called "Going Deeper with
Convolutions." [118]
"What are these dimension-reduction layers?"
No worries, we’ll get back to it in the "Inception Modules" section.
ILSVRC-2015
The 2015 edition [119] popularized residual connections in its aptly named
architecture: Res(idual) Net(work). The training data used in the competition
502 | Chapter 7: Transfer Learning