Let's briefly discuss a few of the popular architectures that I use for transfer learning.
The first and most popular is probably veggie which stands for Visual geometry group.
This is just the name of the research team that created it.
When you look at the architecture for the G.G. although it got state of the art results at the time
it's not that different from what we already know.
It's just the usual CNN with an unusually large number of layers.
At least it was a large number of layers compared to other neural networks.
At the time it was created.
So here's how it looks.
First we have to convolutions followed by pooling.
We repeat that once then we have three convolutions followed by pulling and we repeat that block three
times.
Finally we have three fully connected or dense layers.
As you can see it's pretty much the same thing as the Lina just bigger.
We have a series of convolutions and pulling followed by a few dense layers.
Note that there are a few variations of the veggie network such as VEGF 16 and visa 19 which have 16
layers and 19 layers respectively.
Another common choice is resonant resonant is an even larger network than V.
We won't go into too much detail in this course since our objective is not to understand resonate but
just to know what our options are.
The idea behind resonant is that it's a neuron that work with branches each branch is responsible for
learning something different.
It turns out that one of the branches is just the identity function.
So the other branch is responsible for learning the residual hence the name residual network like Fiji
resident also has different variations with different numbers of layers.
There's resident 50 with 50 layers resonate one to one with one to one layers and resonate 152 which
has 152 layers.
There are also some resonates with different architectures such as resonant V2 and a rest next
yet another common choice is inception.
The layout of Inception is similar to resonate in that you have multiple parallel branches but with
Inception The concept is a little different with Inception instead of just a single convolution going
into another convolution you'll do multiple convolutions in parallel.
You recall that one of the hyper parameters we have to choose is the filter size.
Well inception says why not simply try them all and concatenate the result.
So for example you'll have a one by one filter a three by three filter and a five by five filter and
kind of all the same input image by all these filters.
Once you've done that you'll take the resulting images and stack them all together.
The idea behind the inception network is that you'll have multiple layers of Inception blocks just like
how the resonant has multiple layers of residual learning blocks.
Now I know a lot of people are going to ask Well which one should I choose e.g. resonant or inception.
And the answer is as always you have to try it on your specific dataset with your specific task.
There is no way to predict which one will work better for each use case
another popular appreciate model used for transfer learning is called Mobile on that which specializes
in being lightweight.
So this one actually does have a specific purpose because it's lightweight.
There's a tradeoff made between a speed and accuracy.
Generally speaking the ideal use case for mobile on that is if you want to use convolution or none that
works on less powerful machines such as mobile devices and embedded devices.