Covers: theory of convolutions
Estimated time needed to finish: 30 minutes
Questions this item addresses:
  • What is pooling and padding?
  • What other 2D convolution techniques do exist?
  • When do we use padding and how?
  • When do we use pooling and how?
  • What are dilated convolutions and how they differ from standard convolutions?
How to use this item?

In this video, you will learn about

  • Padding
  • Pooling
  • Dilated Convolutions

What is padding?

When we apply 2D conv to an imagen we tend to lose pixels located on the perimeter of our image. That's where padding is useful, adding some border to extract better values. Also, padding can be apply to data (e.g. images) when dimensionality of differs. specifically, imagine two images of different sizes. Padding can make the smaller image of the same size as the other one without croping or any other way when some information would be lost. Alt text

What is pooling?

When we deal with image datasets the computer tends to have many parameters inside. So we can apply Pooling to downsample the parameters inside our net. Alt text

Dialeted Convolutions

"Dilated Convolutions are a type of convolution that “inflate” the kernel by inserting holes between the kernel elements. An additional parameter (dilation rate) indicates how much the kernel is widened. There are usually spaces inserted between kernel elements." Fisher Yu and Vladlen Koltun

in this blog post , Ferenc Huszár explains the advantage and usage of dialated convolutions. In a nut shell, in his words, "it allows for very large receptive fields while only growing the number of parameters logarithmically".

dialetedconvolutions

Fail to play? Open the link directly: https://youtu.be/2wkKsrFkwwQ
Author(s) / creator(s) / reference(s)
Amir Hajian
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Extracting Features Using Convolution And Regularization

Contributors
Total time needed: ~4 hours
Objectives
Feature extraction via convolution kernels
Potential Use Cases
Building a DL architecture for for computer vision model.
Who is This For ?
INTERMEDIATE
Click on each of the following annotated items to see details.
Resources6/7
VIDEO 1. Mathematics of Convolution
  • Why wee need more than MLP?
  • What is a convolution and kernel?
  • What is a Hann function and how to apply it?
  • What are ConvNets and what is their architecture?
31 minutes
VIDEO 2. Why Convolutions? Sobel & Scharr Filters
  • Why to use Convolutions?
  • What is an image filter?
  • What is image segmentation and how is it done?
  • What convolution does really do?
  • What are two famous and edge detection filters / algorithms?
16 minutes
VIDEO 3. 2D Convolutions, Pooling, and Dilated Convolutions
  • What is pooling and padding?
  • What other 2D convolution techniques do exist?
  • When do we use padding and how?
  • When do we use pooling and how?
  • What are dilated convolutions and how they differ from standard convolutions?
30 minutes
VIDEO 4. Conv-Nets
  • How can I apply Sobel and Scharr Operator on image?
  • What is the difference between Sobel and Scharr Operator and how can I visually compare them?
  • What is a Convolutional Neural Network (CNN)?
  • How can one understand a CNN?
20 minutes
VIDEO 5. Regularization using Dropouts
  • Why do we need dropouts?
17 minutes
REPO 6. Hands-on Convolutional Networks
  • How to implement convnets in PyTorch?
30 minutes
RECIPE 7. Understanding Convolution
40 minutes

Concepts Covered

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