This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.
More Edge Detection
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來自 deeplearning.ai 的課程
Convolutional Neural Networks
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從本節課中
Foundations of Convolutional Neural Networks
Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.
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Andrew NgCo-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Head Teaching Assistant - Kian KatanforooshAdjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
Teaching Assistant - Younes Bensouda MourriMathematical & Computational Sciences, Stanford University, deeplearning.ai
You've seen how the convolution operation allows you to implement a vertical
edge detector.
In this video, you'll learn the difference between positive and negative edges, that
is, the difference between light to dark versus dark to light edge transitions.
And you'll also see other types of edge detectors,
as well as how to have an algorithm learn,
rather than have us hand code an edge detector as we've been doing so far.
So let's get started.
Here's the example you saw from the previous video, where you have this image,
six by six, there's light on the left and dark on the right,
and convolving it with the vertical edge detection filter results in detecting
the vertical edge down the middle of the image.
What happens in an image where the colors are flipped,
where it is darker on the left and brighter on the right?
So the 10s are now on the right half of the image and the 0s on the left.
If you convolve it with the same edge detection filter,
you end up with negative 30s, instead of 30 down the middle, and
you can plot that as a picture that maybe looks like that.
So because the shade of the transitions is reversed,
the 30s now gets reversed as well.
And the negative 30s
shows that this is a dark to light rather than a light to dark transition.
And if you don't care which of these two cases it is,
you could take absolute values of this output matrix.
But this particular filter does make a difference between the light to dark
versus the dark to light edges.
Let's see some more examples of edge detection.
This three by three filter we've seen allows you to detect vertical edges.
So maybe it should not surprise you too much that this
three by three filter will allow you to detect horizontal edges.
So as a reminder, a vertical edge according to this filter, is a three by
three region where the pixels are relatively bright on the left part and
relatively dark on the right part.
So similarly, a horizontal edge would be a three by three region where the pixels
are relatively bright on top and relatively dark in the bottom row.
So here's one example, this is a more complex one,
where you have here 10s in the upper left and lower right-hand corners.
So if you draw this as an image, this would be an image which is going to be
darker where there are 0s, so I'm going to shade in the darker regions, and
then lighter in the upper left and lower right-hand corners.
And if you convolve this with a horizontal edge detector, you end up with this.
And so just to take a couple of examples,
this 30 here corresponds to this three by three region,
where indeed there are bright pixels on top and darker pixels on the bottom.
It's kind of over here.
And so it finds a strong positive edge there.
And this -30 here corresponds to this region,
which is actually brighter on the bottom and darker on top.
So that is a negative edge in this example.
And again, this is kind of an artifact of the fact that we're working
with relatively small images, that this is just a six by six image.
But these intermediate values, like this -10, for
example, just reflects the fact that that filter here, it captures part
of the positive edge on the left and part of the negative edge on the right, and
so blending those together gives you some intermediate value.
But if this was a very large,
say a thousand by a thousand image with this type of checkerboard pattern,
then you won't see these transitions regions of the 10s.
The intermediate values would be quite small relative to the size of the image.
So in summary, different filters allow you to find vertical and horizontal edges.
It turns out that the three by three vertical edge detection filter
we've used is just one possible choice.
And historically, in the computer vision literature,
there was a fair amount of debate about what is the best set of numbers to use.
So here's something else you could use, which is maybe 1, 2,
1, 0, 0, 0, -1, -2, -1.
This is called a Sobel filter.
And the advantage of this is it puts a little bit more weight to the central row,
the central pixel, and this makes it maybe a little bit more robust.
But computer vision researchers will use other sets of numbers as well,
like maybe instead of a 1, 2, 1, it should be a 3, 10, 3, right?
And then -3, -10, -3.
And this is called a Scharr filter.
And this has yet other slightly different properties.
And this is just for vertical edge detection.
And if you flip it 90 degrees, you get horizontal edge detection.
And with the rise of deep learning, one of the things we learned is that when
you really want to detect edges in some complicated image, maybe you don't
need to have computer vision researchers handpick these nine numbers.
Maybe you can just learn them and treat the nine numbers of this matrix
as parameters, which you can then learn using back propagation.
And the goal is to learn nine parameters so that when you take the image,
the six by six image, and convolve it with your three by three filter,
that this gives you a good edge detector.
And what you see in later videos is that by just treating these nine numbers as
parameters, the backprop can choose to learn 1, 1, 1, 0, 0, 0,
-1,-1, if it wants, or learn the Sobel filter or learn the Scharr filter,
or more likely learn something else that's even better at
capturing the statistics of your data than any of these hand coded filters.
And rather than just vertical and horizontal edges,
maybe it can learn to detect edges that are at 45 degrees or
70 degrees or 73 degrees or at whatever orientation it chooses.
And so by just letting all of these numbers be parameters and learning them
automatically from data, we find that neural networks can actually learn low
level features, can learn features such as edges, even more robustly than
computer vision researchers are generally able to code up these things by hand.
But underlying all these computations is still this convolution operation,
Which allows back propagation to learn whatever three by three filter it wants
and then to apply it throughout the entire image, at this position, at this position,
at this position, in order to output whatever feature it's trying to detect.
Be it vertical edges, horizontal edges, or edges at some other angle or
even some other filter that we might not even have a name for in English.
So the idea you can treat these nine numbers as parameters to be
learned has been one of the most powerful ideas in computer vision.
And later in this course, later this week, we'll actually talk about the details of
how you actually go about using back propagation to learn these nine numbers.
But first, let's talk about some other details, some other variations,
on the basic convolution operation.
In the next two videos, I want to discuss with you how to use padding as well as
different strides for convolutions.
And these two will become important pieces of this convolutional building block of
convolutional neural networks.
So let's go on to the next video.
