Questions tagged [convolution-arithmetic]

For questions related to the arithmetic of convolution operations (1d, 2d, 3d convolutions, transposed, etc.) in the context of convolutional neural networks.

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How do I compute the convolution of two kernels of the same size in practice?

Suppose I have a 256-by-256 input matrix called $X$ and two 3-by-3 kernels called $K_1$ and $K_2$. By the associativity of convolution \begin{equation} (X \star K_1) \star K_2 = X \star (K_1 \star K_2)...
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Are the output dimensions of the first and second convolutional layer in YOLO paper correct?

I was reading the last version of the YOLO paper available in Arxiv, and I don't fully understand the output dimensions (I understand width and height, but not depth) of the first and second ...
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Why do we add 1 in the formula to calculate the shape of the output of the convolution?

In the formula to calculate output shape of tensor after convolution operation $$ W_2 = (W_1-F+2P)/S + 1, $$ where: $W_2$ is the output shape of the tensor $W_1$ is the input shape $F$ is the filter ...
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How to calculate the number of parameters of a convolutional layer?

I was recently asked at an interview to calculate the number of parameters for a convolutional layer. I am deeply ashamed to admit I didn't know how to do that, even though I've been working and using ...
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How is the depth of the filters of convolutional layers determined? [duplicate]

I am a bit confused about the depth of the convolutional filters in a CNN. At layer 1, there are usually about 40 3x3x3 filters. Each of these filters outputs a 2d array, so the total output of the ...
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How to compute the number of weights of a CNN?

How can we theoretically compute the number of weights considering a convolutional neural network that is used to classify images into two classes: INPUT: 100x100 gray-scale images. LAYER 1: ...
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Does each filter in each convolution layer create a new image? [duplicate]

Say I have a CNN with this structure: input = 1 image (say, 30x30 RGB pixels) first convolution layer = 10 5x5 convolution filters second convolution layer = 5 3x3 convolution filters one dense layer ...
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Neural Nets: CNN confirming layer/filter arithmetic [duplicate]

I was hoping someone could just confirm some intuition about how convolutions work in convolutional neural networks. I have seen all of the tutorials on applying convolutional filters on an image, but ...
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In the inception neural network, how is an image of shape $224 \times 224 \times 3$ converted into one of shape $112 \times 112 \times 64$?

According to the original paper on page 4, $224 \times 224 \times 3$ image is reduced to $112 \times 112 \times 64$ using a filter $7 \times 7$ and stride $2$ after convolution. $n \times n = 224 \...
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How is the depth of the input related to the depth of the output of a convolutional layer? [duplicate]

Let's suppose I have an image with 16 channels that goes to a convolutional layer, which has 3 trainable $7 \times 7$ filters, so the output of this layer has depth 3. How does the convolutional layer ...
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How can 3 same size CNN layers in different ordering output different receptive field from the input layer?

Below is a quote from CS231n: Prefer a stack of small filter CONV to one large receptive field CONV layer. Suppose that you stack three 3x3 CONV layers on top of each other (with non-linearities in ...
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How is the depth of a convolutional layer determined?

I am looking at a diagram of ZFNet below, in an attempt to understand how CNNs are designed. In the first layer, I understand the depth of 3 (224x224x3) is the number of color channels in the image. ...
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