Questions tagged [filters]

For questions related to the filters (also known as kernels) of a convolutional layer (of a convolutional neural network) or, in general, used in a convolution operation.

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In CNNs, why do we sum the filter derivatives w.r.t the loss function to get the final gradient?

In a Convolutional Neural Network, unlike the fully connected layers, the same filter is used multiple times on the input while convolving - so during backpropagation, we get multiple derivatives for ...
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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 ...
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Does replacing 3x3 filters with 3x1 and 1x3 filters improve the performance?

Recently I have come up with a VGG16 model for my binary classification task. I have relatively simple signal images Therefore (maybe?) other deeper models like ...
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29 views

How to construct input dependent convolutional filter?

I am constructing a convolutional variational autoencoder for images, starting out with mnist digits. Typically I would specify convolutional layers in the following way: ...
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58 views

What is the intuition behind the number of filters/channels for each convolutional layer?

After having chosen the number of layers for a convolutional neural network, we must also choose the number of filters/channels for each convolutional layer. The intuition behind the filter's spatial ...
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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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34 views

How can I implement 2D CNN filter with channelwise-bound kernel weights?

I would like to bind kernel parameters through channels/feature-maps for each filter. In a conv2d operation, each filter consists of HxWxC parameters I would like to have filters that have HxW ...
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1answer
40 views

Do all filters of the same convolutional layer need to have the same dimensions and stride?

In Convolutional Neural Networks, do all filters of the same convolutional layer need to have the same dimensions and stride? If they don't, then it would seem the channel produced by each filter ...
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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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46 views

Does the number of parameters in a convolutional neuronal network increase if the input dimension increases?

If I have a convolutional neuronal network, does the input dimension change the number of parameters? And if yes, why? If the sizes and lengths of the filters are still the same, how can the number of ...
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How does the math behind heat map filters work?

I am working on an app that generates heat/ thermal map given a picture. i have been able to get what i expected using python opencv builtin function ...
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What is the point of using convolutions of kernel size 1, or 1x1, etc?

I understand the gist of what convolutional networks do and what they are used for, but I still wrestle a bit with how they function on a conceptual level. For example, I get that filters with kernel ...
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85 views

What happens to the channels after the convolution layer?

I wonder what happens to the 'channels' dimension (usually 3 for RGB images) after the first convolution layer in CNNs? In books and other sources, it is always said that the depth of the output ...
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YOLOv3 Model Structure: Why is filters = (classes + coords + 1) * num?

Here's a tutorial about doing custom training of YOLO (Darknet): https://medium.com/@manivannan_data/how-to-train-yolov3-to-detect-custom-objects-ccbcafeb13d2 The tutorial guides how to set values in ...