# Questions tagged [convolution]

For questions related to the convolution operation in mathematics, convolutional neural networks, image processing and computer vision.

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### 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 $5 \times 5$ ...
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### Why can we perform graph convolution using the standard 2d convolution with $1 \times \Gamma$ kernels?

Recently I was reading this paper Skeleton Based Action RecognitionUsing Spatio Temporal Graph Convolution. In this paper, the authors claim (below equation (\ref{9})) that we can perform graph ...
45 views

### Is it possible to apply the associative property of the convolution operation when it is followed by a non-linearity?

The associative property of multidimensional discrete convolution says that: $$Y=(x \circledast h_1) \circledast h_2=x\circledast(h_1\circledast h_2)$$ where $h_1$ and $h_2$ are the filters and $x$ is ...
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### Why do the inputs and outputs of a convolutional layer usually have the same depth?

Here's the famous VGG-16 model. Do the inputs and outputs of a convolutional layer, before pooling, usually have the same depth? What's the reason for that? Is there a theory or paper trying to ...
28 views

### Is it possible to express attention as a Fourier convolution?

Convolutions can be expressed as a matrix-multiplication (see e.g. this post) and as an element-wise multiplication using the Fourier domain (https://en.wikipedia.org/wiki/Convolution_theorem). ...
48 views

### Embedding Layer into Convolution Layer

I'm looking to encode PDF documents for deep learning such that an image representation of the PDF refers to word embeddings instead of graphic data So I've indexed a relatively small vocabulary (88 ...
54 views

### Can I think graph convolution as 2D convolution like images?

Kipf et al described in his paper that we can write graph convolution operation like this: $$H_{t+1} = AH_tW_t$$ where, $A$ is the normalized adjacency matrix, $H_t$ is the embedded representation of ...
37 views

### Why does the number of channels in the PointNet increase as we go deeper?

For example, in PointNet, you see the 1D convolutions with the following channels 64 -> 128 -> 1024. Why not e.g. ...
20 views

### Is there a difference between using 1d conv layers and 2d conv layers with kernel with size of 1 along other than time dimension?

Let's assume I use convolutional networks for time-series prediction. Data I feed to the network have 1 channel depth, height of number of periods and number of features is the width, so the frame ...