# Questions tagged [convolutional-layers]

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

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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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### How to implement a (3 + 2)-dimensional convolutional layer where the 2d space is "internal"?

I am trying to train a CNN to learn 5D (kind of) data. The data is structured as follows. It has three spatial dimensions [x, y, z], but it also has two "...
152 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 ...
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### How do convolutional layers of basic Graph Convolutional Networks work?

I was reading the following article on Towards Data Science (here) and it says the following, regarding the calculation of convolutional layers: So the overall steps are: Transform the graph into ...
17 views

### 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, ...
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### Is there any animation that illustrates the "fold" and "unfold" operations of convolutional layers?

There are fourteen convolution layers in PyTorch. Among them six are related to convolution, another six are related to transposed convolution. The remaining two are fold and unfold operations. The ...
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### Convolutional Layer Multichannel Backpropagation Implementation

I have been working on coding a CNN in python from scratch using numpy as a semester project and I think I have successfully implemented it up to backpropagation in the MaxPool Layers. However, my ...
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### Error in MobileNet V1 Architecture?

From the architecture table of the first MobileNet paper, a depthwise convolution with stride 2 and an input of 7x7x1024 is followed by a pointwise convolution with the same input dimensions, 7x7x1024....
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### Is the 3d convolution associative given that it can be represented as matrix multiplication?

I'm trying to understand if a 3D convolution of the sort performed in a convolutional layer of a CNN is associative. Specifically, is the following true:  X \otimes(W \cdot Q)=(X \otimes W) \cdot Q, ...
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### How can the FCNN reduce the dimensions of the input from $1048 \times 100$ to $523 \times 100$ with max-pooling?

I am trying to implement a paper on Image tempering detection and localization, the paper is Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks, I was ...
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### 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. ...
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### Can fully connected layers be used for feature detection?

I need help in understanding something basic. In this video, Andrew Ng says, essentially, that convolutional layers are better than fully connected (FC) layers because they use fewer parameters. But I'...
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### When to use convolutional layers as opposed to fully connected layers?

I am still new to CNNs, but I would like to check my understanding between when to use convolutional layers versus fully connected layers. From what I have read, we can use convolutional layers with ...
23 views

### Wasserstein GAN with non-negative weights in the critic

I want to train a WGAN where the convolution layers in the critic are only allowed to have non-negative weights (for a technical reason). The biases, nonetheless, can take both +/- values. There is no ...
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### What is the significance behind having small kernel sizes over having one large kernel size that covers the entire input in a CNN?

I have hardly ever seen anyone cover the entire input image with a filter of the same dimensions. I was wondering why that is the case, and if the performance in say, an image detection application ...