Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [convolutional-layers]

For questions related to convolutional layers, which are layers that perform the convolution (or cross-correlation) operation.

52 questions
10
votes
2 answers

When should I use 3D convolutions?

I am new to convolutional neural networks, and I am learning 3D convolution. What I could understand is that 2D convolution gives us relationships between low-level features in the X-Y dimension, while the 3D convolution helps detect low-level…
Shobhit Verma
  • 161
  • 1
  • 7
8
votes
2 answers

What is the point of using 1D and 2D convolutions with a kernel size of 1 and 1x1 respectively?

I understand the gist of what convolutional neural networks do and what they are used for, but I still wrestle a bit with how they function on a conceptual level. For example, I get that filters with kernel size greater than 1 are used as feature…
Arcturai
  • 81
  • 1
7
votes
3 answers

How to compute the derivative of the error with respect to the input of a convolutional layer when the stride is bigger than 1?

I read that to compute the derivative of the error with respect to the input of a convolution layer is the same to make of a convolution between deltas of the next layer and the weight matrix rotated by $180°$, i.e. something…
volperossa
  • 161
  • 7
6
votes
2 answers

How to calculate the number of parameters of a convolutional layer?

I was recently asked at an interview to calculate the number of parameters for a convolutional layer. I am deeply ashamed to admit I didn't know how to do that, even though I've been working and using CNN for years now. Given a convolutional layer…
Ælex
  • 195
  • 1
  • 7
5
votes
1 answer

How to add a dense layer after a 2d convolutional layer in a convolutional autoencoder?

I am trying to implement a convolutional autoencoder with a dense layer at the bottleneck to do some dimensional reduction. I have seen two approaches for this, which aren't particularly scalable. The first was to introduce 2 dense layers (one at…
Pavan Inguva
  • 71
  • 1
  • 2
4
votes
2 answers

How is the depth of the input related to the depth of the output of a convolutional layer?

Let's suppose I have an image with 16 channels that goes to a convolutional layer, which has 3 trainable $7 \times 7$ filters, so the output of this layer has depth 3. How does the convolutional layer go from 16 to 3 channels? What mathematical…
Du Bois Eloi
  • 43
  • 4
4
votes
1 answer

Are these visualisations the filters of the convolution layer or the convolved images with the filters?

There are several images related to convolutional networks on the Internet, an example of which I have given below My question is: are these images the weights/filters of the convolution layer (the weights that are learned in the learning process),…
Ali.kavari76
  • 111
  • 6
4
votes
2 answers

Is the stride applied both in the horizontal and vertical directions in convolutional neural networks?

In the convolutional layer for CNNs, when you specify the stride of a filter, typical notes show some examples of this but only for the horizontal panning. Is this same stride applied for the vertical direction too when you're done with the current…
anonuser01
  • 321
  • 1
  • 3
  • 13
4
votes
1 answer

Do all filters of the same convolutional layer need to have the same dimensions and stride?

In Convolutional Neural Networks, do all filters of the same convolutional layer need to have the same dimensions and stride? If they don't, then it would seem the channel produced by each filter would have different sizes. Or is there some way to…
David
  • 293
  • 2
  • 5
3
votes
1 answer

What is the use of the regular convolutional layer in expansion path of U-Net?

I was going through the paper on U-Net. U-net consists of a contracting path followed by an expanding path. Both the paths use a regular convolutional layer. I understand the use of convolutional layers in the contracting path, but I can't figure…
Bhuwan Bhatt
  • 394
  • 1
  • 11
3
votes
1 answer

What is the intuition behind the number of filters/channels for each convolutional layer?

After having chosen the number of layers for a convolutional neural network, we must also choose the number of filters/channels for each convolutional layer. The intuition behind the filter's spatial dimension is the number of pixels in the image…
yuri
  • 33
  • 2
3
votes
0 answers

What does "convolve k filters" mean in the AlphaGo paper?

On page 27 of the DeepMind AlphaGo paper appears the following sentence: The first hidden layer zero pads the input into a $23 \times 23$ image, then convolves $k$ filters of kernel size $5 \times 5$ with stride $1$ with the input image and applies…
William Ehlhardt
  • 31
  • 1
2
votes
2 answers

Is this aggregation of multiple convolutions of the same input a type of attention or dynamic convolution?

Are there any examples of people performing multiple convolutions at a single depth and then performing feature max aggregation as a convex combination as a form of "dynamic convolutions"? To be more precise: Say you have an input x, and you…
ADA
  • 165
  • 4
2
votes
2 answers

How can equivariance to translation be a benefit of a CNN?

I just learnt about the properties of equivariance and invariance to translation and other transformations. Being invariant to translation is clearly an advantage, as even if the input gets shifted, the network will still learn the same features,…
Aether
  • 265
  • 2
  • 7
2
votes
1 answer

Why do we add 1 in the formula to calculate the shape of the output of the convolution?

In the formula to calculate output shape of tensor after convolution operation $$ W_2 = (W_1-F+2P)/S + 1, $$ where: $W_2$ is the output shape of the tensor $W_1$ is the input shape $F$ is the filter size $P$ is the padding $S$ is the stride. Why…
adityagabbar
  • 23
  • 4
1
2 3 4