CODEWITHSUNDEEP
neural-networkconv-neural-networkconvolution
Ruby
Ruby

Reputation: 61

When to use what type of padding for convolution layers?

I know when we are using convolution layers in a neural net we usually use padding and mainly constant padding(e.g. zero padding). And there are different kinds of padding(e.g. symmetric, reflective, constant). But I am not sure what are the advantages and disadvantages of using different padding methods and when to use which one.

Upvotes: 5

Views: 4698

Answers (1)

j35t3r
j35t3r

Reputation: 1523

it really depends on the situation for what the neural network is intended. I would not tell it pros and cons. This time the world cannot put into a binary scheme.

I will give you some interesting links:

https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/

http://web.stanford.edu/class/cs20si/lectures/

When you try to design a network, then start to think about what it should be designed for. Then, you try some things, it will be logical that , in case of convolutional networks, valid padding makes the image smaller and full padding makes the image bigger, but it uses, e.g zero padding, what adds 0 at the edges and could lead to veils... and so on... you must try a lot...

For pixelwise deep convolutional networks, people use valid, such as semantic segmentation. No/less "smear-effect". For object detection, people use same, only a bounding box is needed for the detected object.

HTH

Upvotes: 3

Related Questions