Somehow learns to detect human faces. And then it sees
a gray stripe, like underneath the human face and the
correct proportions, the correct size of stripe
compared to the face, then like maybe that's a seat
belt. There's other categories to like cowboy hat. So
the network learns to recognize cowboy hats. The point
is the network learns the concept of human faces,
simply because it makes it better and its job, which
is recognizing other categories. Oh, some other fun
stuff. So this is the first layer, like I said,
simple patterns. These are all very simple patterns.
I'll show you some more complicated patterns up here
in the fifth layer. Okay, so here's the fifth
convolution layer. By this layer, the network learns
some pretty abstract stuff. So in fact, there's a few
pattern detectors here that are four phases. I can
scroll down to one of my favorites here. Get this guy
here. Cool. So that should be a face detector. You see
it like following my face left and right. Sort of
Yeah. I can point it to you guys. We can see if it'll,
if you have faces too. Okay, so first thing, if it's
blurry, it doesn't work very well, if the faces are
tiny. So here you can see to the network, all your
faces in the back are quite small. So it's actually
not really firing there at all. But if we point it to
people whose faces are bigger to the network, we can
see it fires for multiple people. Couple of you there.
Anyone else want to try? Yeah, okay. Okay. So it's a
face detector. Cool, right? So the question is, why
does the network learn a face detector? It could be
the answer. The answer could be that we trained it to
learn faces. But actually this network we never
trained it to learn faces. There's no category in
image net for humans. It's actually it's kind of
weird. Humans are just like background background, the
network So why would the network learn faces? Well, we
did teach it to learn some other categories that are
quite human related. So one of them that I like is
seatbelt. Why a seatbelt the category? I don't know, I
didn't make the data set. So think about like in pixel
space, just from an abstract art perspective or
whatever. What does the seat belt look like? So seat
belts, imagine I'm wearing the seat belt, and you can
see it right here. It's basically like this gray
stripe. Right? So what happens to the network if it
goes around saying every gray stripe is a seat belt?
Well, let's see here. Let's just imagine we had this
random pictures input to the network. So how many like
sort of sideways gray stripes or like approximately
gray stripes you can see right, there's like some
stripe there. There's some stripe there. There's some
stripe there in there. So if the network goes around
saying every like gray stripe, it seizes a seatbelt,
it'll be mostly wrong. On the other hand, if it
somehow learns to detect human faces, And then it sees
a gray stripe, like underneath the human face in the
right proportions, the correct size of stripe compared
to the face, then like maybe that's a seat belt.
There's other categories to like cowboy hat. So the
network learns to recognize cowboy hats. cowboy hats
are pretty simple, but like a couple of head is
usually right on top of the human face. The point is
the network learns the concept of human faces, simply
because it makes it better at its job, which is
recognizing other categories. Alright, we can see we
can find some other fun, fun. neurons in here. There's
one that recognizes text. So does anyone have any
simple thing with text on it? Here Here is perfect.
Okay, good. Okay, so see if you can find the neuron.
It's somewhere in here. It'll be responding when you
see like letters on the screen.
See anything.
It's not a ton of text. Someone's pointing I can't
tell where you're pointing. It's all good. So the one
I'm thinking of is right here, number four. So there's
not too much text here. In fact, it's just this guy
here. Maybe I'll try some you wrote, okay, so it's,
it's definitely more tuned for printed text than like,
handwritten text. Okay, there you go. There you go.
Okay, I'm just gonna pause that. Thanks. So again,
here's a neuron halfway through this network that's
doing something really cool one that detect faces even
though we never told it to detect faces. Now we have
one detecting text. follow up question, Why in the
world would it learn to detect text? So it's the same
same story I told you before about the seatbelt. It
turns out there are certain categories in image net,
like camera lens, and here parking meter. So camera
lens in particular with the camera lens. So in pixel
space, kind of looks like camera lenses are mostly
just like black circles. So if a network goes around
calling every black circle a camera lens, it's going
to be mostly wrong because there are like lots of
circular black things in the world. But if it finds a
black circle where there's like some text right in the
middle, suddenly black circle plus text equals
cameraman's. So now by detecting text, it's becoming
good at its original job of detecting camera lens. I
think there was a question. Oh, yeah, come up. Come
up, sir. It's always fun like in different talks. I
try to find different things. Usually.
Yeah, here Here, try.
So depending on the talk, it's always fun to like try
to find something to use for these demos. The working
the back Okay, so this is in the front. You can see
This text is kind of responding. It can't see what's
at the top. Oh, it says Draper. Yeah, yeah, exactly.
On the back. It's relatively more full of text. The
more you play around to this more you can find out for
example, like if you move it too much, it ends up
being like blurry. And because the text is so small
and even like a couple pixels of blurriness, basically
like destroys, it's a religious see. Fortunately, the
data set it's trained on is mostly photos that are
like quite crisp from flicker and so on. In the most
desperate talks after pull out like my driver's
license or something so
glad we can avoid that.
