Now, let's take a look at the Inception network has a very cool name, doesn't it?
It's also called Google in that architecture.
So but it's more popularly known as the Inception network.
So let's take a look at what problem this solved.
So as you've seen before, it's a lot of parameter tweaking involved and CNN's, isn't it?
We have things like filter sizes, stride padding, fully connected layers, sizes.
So inception aim to solve the first part of this problem.
The filter size problem because remember, you can use tree by tree five by five, seven by seven,
depending on what type of features in your image.
But thing is, you don't know.
So Inception basically proposed a solution that looks like this.
So introduced in bay in 2014 by Google, it would achieve the state of the art performance on the image
and data set on in the 2014 to 2014 challenge.
So how did it do it?
Basically, it would use something called the inception model here.
There's no vision here, and that is inception module with dimension reductions, which is quite important
as well.
We'll discuss that.
No.
So how does Inception use multiple filters?
Well, this is what it does.
It takes applies multiple filters through the input image here.
Maintaining a straight and putting of one does to get maintain consistency, and then it produces this
final output of feature maps here.
But it's basically a block of feature maps at this point, so you can have different number of sizes
as well.
So we get a final output of 28 by 28 by 256 in this case here.
So you can see we're using multiple multiple filter sizes here, but there's a problem with this.
So what's the problem?
The problem is computation.
To do something like this is very computationally expensive.
It's a hundred and twenty million computations required to consider that right.
That explains it.
So how do we reduce this?
Well, by using one by one convolutions to reduce the competition course?
So remember in mobile that we use something called point ways convolutions.
Well, it's a similar concept here, actually.
We use something called a bottleneck lelo to implement it a bit differently so that one by one convolution
block now is between the initial the previous conflict here.
This is defined by five one.
So it's not a point of filter point in the middle at bottlenecks it.
And this greatly reduces to calculations here because no, at this kind block, we have 2.4 million
computations.
And then in this second conflict, because of the output that it produces, we get ten million calculations
or computations.
So we shrink the representation and then increase the size afterward by doing this.
And this gives us a 10x computation cost.
This is what it looks like here.
You can see they have it's assumed as the previous activation here.
So this is the feature maps of some previous layer.
We have the one by one, one by one convolution here going into the five by five entry by tree.
We don't have it here because it's already a one by one convolution.
And then we concatenate all the outputs here.
We have a are playing max maximal here as well and pass it to a another one by one convolution.
This gives us the full size that we expected before that size was this size here.
Twenty eight by twenty by 256.
And then we go.
That's how we actually saw the inception block looks like.
And then ception network design basically has a bunch of these inception blocks, as you can see here
over and over.
This is what I've circled here and read.
And what are these hidden pink?
Though these are side branch, these side branches basically apply a regularization effect to the network,
so it helps inception as well.
So it's it's quite good.
And there are many tweaks in this network to variations basically including the improvement of the evolution
of the inception network.
It went through a very fast evolution, in my opinion.
But then again, these things happen in the deep learning world is when when you release a paper and
then other authors review it and give you give you feedback, you can make some quick improvements,
which is what most likely happened from Inception version one, two and three to one we looked at in
this slide was actually a vision tree.
And there's also a vision for which includes a resonant module inside of it.
So remember we did residents before where we have the Short-Circuit module that was basically incorporated
in Inception version four.
And you can take a look at the history of it here.
This is an interesting link by this guy in the cool of the league.
He has a he's one of the Google Guys who published who is of the publication of Inception.
And here's a fun fact Why is it called inception?
Well, remember Christopher Nolan's movie with Leonardo DiCaprio called Inception that came out in 2013
and 2014?
Remember, it is a fun meme, and it actually was cited in the paper where we were talking about dreams,
and he was saying, we need to go deeper.
That's basically analogous to performance back then of CNN's where everyone was saying, If we want
to get better performance, you just have to make deeper networks.
So Inception basically took that and made fun of it.
But Inception does solve some of the problems with going deeper, just like resonance.
So that's why it was such a popular network, too.
So we'll stop there.
And next one, take a look at Squeeze It, which is a mobile, a little high efficiency, low size,
little parameters CNN meant for embedded devices and mobile devices.
So I'll see you in that section shortly.
Thank you.