Questions tagged [convolution]

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

7 questions with no upvoted or accepted answers
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Why would you implement the position-wise feed-forward network of the transformer with convolution layers?

The Transformer model introduced in "Attention is all you need" by Vaswani et al. incorporates a so-called position-wise feed-forward network (FFN): In addition to attention sub-layers, each of the ...
2
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1answer
31 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 ...
2
votes
1answer
79 views

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 ...
1
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0answers
11 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 ...
1
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0answers
21 views

How do we choose the filters for the convolutional layer of a convolution neural network?

Since the hidden layers of a CNN work as a trainable feature extractor, more detailed content based on a larger number of pixels shall require bigger filter sizes. But for cases where localized ...
1
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0answers
120 views

What's the difference in using multiple convolutional layers and no pooling versus using a single convolutional layer and a single max pooling layer?

I'm currently working on a college project in which I'm designing a Deep Q-Network that takes images/frames as an input. I've been searching online to see how other people have designed their ...
0
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0answers
8 views

Looking for the right type of 1D-Convolution that only considers one column/attribute

My input has the shape of n rows (time steps) and m columns (attributes). I want to train a convolutional neural network on it to predict a class. I am currently using 1D-Convolutions. I got a good ...