How Convolution Works

Показать описание

A guided tour through convolution in two dimensions for convolutional neural networks and image processing

Brandon Rohrer

Рекомендации по теме

Комментарии

I spent hours to learn about the concept of Convolution and didn't find answer how does it work in image processing, but you gave us probably the best closet to practicable definition.

naumanshafique

Magically clear. Finally got what convolution is all about. I appreciate your hard work.

canklc

My most sincere kudos. Your way of explaining things is how teaching should be. Looking forward to learning more from you.

turpialito

This is the best explanation I have found to date on convolutions and the animations were fantastic. Great work.

ejkitchen

Had to watch this a few times to start to get it, but it's a great video. So there are two ways of looking at the convolution. One is where the kernel is the actual thing being convolved and it's saying, 'At each pixel, give me a score for how well the kernel matches at this region.'

The second way is where you're treating the kernel more like a subimage you want to make multiple copies of, as scaled by pixel values in another image. But then to counteract the way kernels pull data into the center of the kernel, you have to reverse the subimage you want to make copies of before you use it as the kernel.

tobuslieven

One nice thing to keep in mind is that the convolution in time/spatial domain is a simple multiplication in frequency domain. And vice versa 😉

podborski

Thanks for this video, great explanation!

chrisminnoy

Convolutions are part of nature as well. We see it in microscopy, where basically point light sources are Convoluted by the lenses in the microscope which gives the point a gaussian blur and some rings around the central blur, caused by diffraction, in the resulting image. The shape is known as an Airy disc.

BjarneThorsted

Brilliant and awe inspiring !! Thank You sir, for such a high quality content.

siddhantpathak

This is really awesome. Keep up the good work :)

jayachandra

Flipping the kernel hurts my brain... and my feelings?? It is very anti-intuitive that when you flip, you get copies that have the SAME orientation as the original...

allyourcode

Thanks for this video. It's very enlightening

qtptnqtptnable

What is the idea of reversed kernel? What happens to convolution if we do not reverse?

ppujari

best video i watched on convolution. now how do we represent it in maths?

taqwaajourney

Hello, first of all thank you very much for the video, it's very interesting and well-structured. I haven't understood one thing though. With the dog image at 16:57, with my calculations, the upper left edge of its head should produce (as result in convolution) a red diagonal line instead of a white one. Because the edge has much more white spots on its down-right side and much less on its upper-left side. Why do we produce the exact reverse (or opposite) value in the convolution? Have a nice day!

PS: I now resent the use of the word ''much'' in everyday language because apparently we can deduce any subject matter to an integer or a series of integers somehow, therefore favoring ''many'' in all cases.

tolgatolgay

... I was writing my thesis* and just now I started writing a chapter about what convolutional filters I use and saw that this video went up.
What kind of sorcery is this?

*only work with "classical" image anysis, no machine learning

bruderdasisteinschwerermangel

your content is amazing. will you do any q and a in your online class?

i am having trouble finding out what “convolution on top of convolution” is.

if you run 20 filters on an image you get another image with 20 channels. what happens when you run 20 filters on those 20 filters after pooling? do each of those filters have 20 channels? do you run 1 filter on 1 channel?

everyone talks about layer 1 but no one really talks about multi layer convolution in any great detail. does your class offer this type of granular analysis?

shake

Nice explanation! I'd like to see if there's some use for blind deconvolution in ML, e.g. for noise filtering, etc.

Icenri

at 8:15 convolution image, it looks like the red diagonal line should be on top, and the white one should be below

rosairefongang

The part where you use kernel to create multiple of itself on the image, is it called Cross-correlation? or just a special form of convolution?( apologies for any of my misunderstanding of cross and conv) Since I heard cross-correlation is regularly used for image matching

jimmythenthusiast

How Convolution Works

All Convolution Animations Are Wrong (Neural Networks)

But what is a convolution?

Convolutional Neural Networks Explained (CNN Visualized)

How Convolution Works

How convolutional neural networks work, in depth

Tutorial 21- What is Convolution operation in CNN?

C4W1L06 Convolutions Over Volumes

What are Convolutional Neural Networks (CNNs)?

[CS316] Lecture 3: From Machine Learning to Deep Learning

Simple explanation of convolutional neural network | Deep Learning Tutorial 23 (Tensorflow & Pyt...

Convolutional Neural Networks (CNNs) explained

CNN: Convolutional Neural Networks Explained - Computerphile

2D Convolution Neural Network Animation

How Convolutional Neural Networks work

What is a convolutional neural network (CNN)?

Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)

Groups, Depthwise, and Depthwise-Separable Convolution (Neural Networks)

Convolution Operation in CNN

Convolutional Neural Networks | CNN | Kernel | Stride | Padding | Pooling | Flatten | Formula

convolution of images

2D Convolution Explained: Fundamental Operation in Computer Vision

Convolutional Neural Network from Scratch | Mathematics & Python Code

How convolution works

convolutional neural network explained | Deep Learning | CNN | neural network | AI