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How to visualize the 4th Dimension of 3D Convolution?
I am working with 3D convolutions for the first time and I would like to understand how the operation works from a geometric point of view.
For example, let’s take a simple 2D Convolutional Kernel with a stride of 2:
I would like to visualize the kernel, to understand how the algorithm works.
So, what I did was translating in the Z axis of the kernel:
I would like to know what is the effect of this operation.
Is it safe to do this? is there any risks to translate the kernel along the z axis?
How could I visualize the other dimensions of the kernel?
A:
Is it safe to do this? is there any risks to translate the kernel along the z axis?
Yes, your “translate” is nothing more than a U layer, once you have added it to your network, and then you feed it with the padded (if needed) input. As far as safety concerns go, the only real reason that it might be unsafe is that the filter might have to deal with a boundary where the padding is inside the filter in order to preserve the class (at least).
More importantly, in order to visualize the 4 dimensions of your filter, you will have to visualize the 4 different dimensions of the data and the filter in the same plot. It is not clear to me if you have done that, but if you haven’t, it should be fairly easy to re-invent this, if you have enough reference images.
The design of your network is not very intuitive. A Convolutional Neural Network is only made out of 3 dimensions, not 4, as is much easier to grasp than 5. So the 4th dimension of your network is not really represented here.
If you want to visualize the process of convolutions, I suggest you start with Understanding Convolutional
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