Linked Questions
12 questions linked to/from Do convolutional neural networks perform convolution or cross-correlation?
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What is the difference between a receptive field and a feature map?
In a CNN, the receptive field is the portion of the image used to compute the filter's output. But one filter's output (which is also called a "feature map") is the next filter's input.
What's the ...
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What is the need for so many filters in a CNN?
Consider the following coding line related to CNNS
Conv2D(64, (3,3), strides=(2, 2), padding='same')
It is a convolution layer with filter size $3 \times 3$ and ...
- 3,201
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Can neurons in MLP and filters in CNN be compared?
I know they are not the same in working, but an input layer sends the input to $n$ neurons with a set of weights, based on these weights and the activation layer, it produces an output that can be fed ...
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Are filters fixed or learned?
No matter what I google or what paper I read, I can't find an answer to my question. In a deep convolutional neural network, let's say AlexNet (Krizhevsky, 2012), filters' weights are learned by means ...
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Why do we need convolutional neural networks instead of feed-forward neural networks?
Why do we need convolutional neural networks instead of feed-forward neural networks?
What is the significance of a CNN? Even a feed-forward neural network will able to solve the image classification ...
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How to mathematically describe the convolution operation (with a Gaussian kernel)?
I have to build a model where I pre-process the data with a Gaussian kernel. The data are an $n\times n$ matrix (i.e one channel), but not an image, thus I can't refer to this matrix as an image and ...
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What are the most common feedforward neural networks?
What are the most common feedforward neural networks? What kind of inputs do they receive? For example, do they receive binary numbers, real numbers, vectors, or matrics? Is there such a taxonomy?
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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 $m$ ...
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What are acting as weights in a convolution neural network?
Looking at some old notes I took on CNN's and I wrote down that the weights in a CNN are acting like filters in a CNN but to be honest I don't really know what the weights are acting as in a CNN and ...
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How is the convolution operation connected to neural networks?
I've been reading up on the convolution operation and neural networks. I understand that the convolution operation is defined as:
$$(f * g)(t)=\int_{-\infty}^{\infty} f(\tau) g(t-\tau) d \tau$$
The ...
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Origins of the name of convolutional neural networks
Convolutional neural networks (CNNs) contain convolutional layers. In modern deep learning libraries such as Tensorflow and PyTorch among others, convolutional layers are implemented by using the ...
- 401
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How many layers and neurons in a FFNN do I need to make it equivalent to a CNN?
I started to learn machine learning early, and I studied the convolutional neural network and its ability to understand images and how it helps to reduce the number of parameters that need to be tuned....
