All Questions
11 questions
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38
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Do all CNNs learn to detect edges in the first layer?
I was looking at 3D CNNs that process volumetric data, e.g. for MRI images of brain, where the input is a 4D tensor, and I couldn't find images from the filters of the first layer.
Suppose that ...
- 231
1
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0
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784
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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 ...
3
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0
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49
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Why might the convolution be inappropriate when the task involves incorporating information from very distant locations in the input?
When I am reading about convolutional neural networks, I have encountered the following sentence from the textbook(page 341) that says about the limitation of the usage of the convolution in CNNs.
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- 187
1
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1
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91
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What is meant by "real-valued argument" in this context of the convolution operation?
Consider the following statement from Deep Learning book (p. 327, chapter 9: Convolutional Networks)
In its most general form, convolution is an operation on two functions
of a real-valued argument.
...
- 3,990
2
votes
1
answer
125
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What do the variables in the cross-correlation formula mean?
I understand what cross-correlation does given a kernel and an input image, but the formula confuses me a little. Given here in Goodfellow's Deep Learning (page 329), I can't quite understand what $m$ ...
1
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1
answer
105
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How will the input be preserved as we go deeper in CNN, where dimensions decrease drastically?
Our length of feature representation decreases as we go deeper into the CNN, I mean to say that horizontal and vertical lengths decrease while depth(channels) increase. So, how will the input be ...
3
votes
1
answer
97
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Is it useful to eliminate the less relevant filters from a trained CNN?
Imagine I have a tensorflow CNN model with good accuracy but maybe too many filters:
Is there a way to determine which filters have more impact in output? I think it should be possible. At least, if ...
- 133
7
votes
1
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2k
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What is the difference between asymmetric and depthwise separable convolution?
I have recently discovered asymmetric convolution layers in deep learning architectures, a concept which seems very similar to depthwise separable convolutions.
Are they really the same concept with ...
- 183
14
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1
answer
17k
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How can the convolution operation be implemented as a matrix multiplication?
How can the convolution operation used by CNNs be implemented as a matrix-vector multiplication? We often think of the convolution operation in CNNs as a kernel that slides across the input. However, ...
- 41.4k
2
votes
1
answer
637
views
In which scenario would you want to have two adjacent pooling layers?
In which scenario, when assembling a CNN, would you want to have two adjacent pooling layers, without a convolutional layer in between?
- 162
3
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1
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4k
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Is my understanding of how the convolution with stride 2 works in this example correct?
I'm currently reading this explanation of convolutional neural networks and there's a part around strides that I don't quite understand. I'm just starting with this, so I apologize if this is a really ...
- 133