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. If you are looking for kernel functions (for example, used in the context of Gaussian processes or SVMs), you use the tag "kernel-functions".
50
questions
0
votes
1
answer
29
views
How to deactivate kernels from a trained CNN model
I trained a 1 layer CNN model with 128 3x3 kernels. I evaluated the model with a prescribed test data set and now I want to evaluate the performance of this model where we only consider select kernels ...
0
votes
0
answers
27
views
Learned kernels in CNN seem just random patterns
I am training a classification neural network using Tensorflow2 (specifications below). The training goes well (good accuracy and no overfitting, apparently). During the training I monitor the learned ...
- 269
2
votes
1
answer
157
views
How to calculate number of connected neurons with filter
let's say I have a conv layer i with 64 feature maps and a filter size of 3x3.
The previous conv layer i-1 has 32 feature map.
Step-size is 2 and padding 1.
My question is now how to know how many ...
- 131
0
votes
1
answer
73
views
CNNs: What does "number of filters" mean?
I understand how depth, kernel size, stride, and padding works when dealing with filters in a spatial convolution layer.
What I don't understand is "the number of filters". Does that mean ...
0
votes
0
answers
20
views
Given the high resolution signal and the low pass filter (kaiser filter), is there a way to reconstruct the low resolution signal?
When we upsampling a discrete 1d signal by 2x, we first interleave the signal by 0, then pass through a low pass filter.
low resolution signal [x1, x2, x3, x4] -> interleave 0 -> [x1, 0, x2, 0, ...
- 101
0
votes
1
answer
56
views
Is there any subtle difference between kernel and filter in the context of neural netowrks?
Consider the following excerpt from a paragraph, taken from the topic Detecting features with convolutions of the textbook named Deep Learning with PyTorch by Eli Stevens et al., regarding ...
- 3,491
1
vote
1
answer
90
views
Graph Convolutional Networks: why are non-parametric filters not localized in space?
I was reading the following paper here about some of the groundwork in graph deep learning. On page 3, in the bit entitled Polynomial parameterization for localized filters, it states that non-...
- 165
1
vote
0
answers
76
views
FCNs: Questions about the filter rarefaction in the CVPR paper [Long et al., 2015]
I am reading the paper about the fully convolutional network (FCN).
I had some questions about the part where the authors discuss the filter rarefaction technique (I guess this is roughly equivalent ...
- 11
1
vote
1
answer
331
views
Is it possible to have different channel dimensions in a CNN?
Let's say I have two channels that I wish to feed into a CNN. One of the channel contains 4 traces and has a width of 512. Stacking them on top of each other therefore yields an image with dimensions (...
1
vote
1
answer
341
views
Given an input of shape $(3, 32, 32)$, which is convolved with a $(3 \times 3)$ kernel, how do I calculate the FLOPS?
I have an input tensor of shape $\mathbf{(3, 32, 32)}$ consisting of 3 channels, 16 rows, and 16 columns. I want to convolve the input tensor using $\mathbf{(3 \times 3)}$ kernel/filter. How can I ...
- 13
0
votes
0
answers
74
views
What is the significance behind having small kernel sizes over having one large kernel size that covers the entire input in a CNN?
I have hardly ever seen anyone cover the entire input image with a filter of the same dimensions. I was wondering why that is the case, and if the performance in say, an image detection application ...
- 223
0
votes
1
answer
171
views
Is "kernel" different from "filter" in convolutional neural networks?
Recently I asked a question on how a convolution 2d layer changes an RGB image into a grayscale image. Assume that our task is to convert an RGB image into a grayscale image. I use to believe that ...
- 3,491
3
votes
0
answers
1k
views
What is the fundamental difference between max pooling and adaptive max pooling used in PyTorch
PyTorch provides max pooling and adaptive max pooling.
Both, max pooling and adaptive max pooling, is defined in three dimensions: 1d, 2d and 3d. For simplicity, I am discussing about 1d in this ...
- 3,491
0
votes
2
answers
53
views
Is pruning only applicable to convolutional neural networks?
This article talks about pruning in the context of convolutional neural networks:
One of the first methods of pruning is pruning entire convolutional filters. Using an L1 norm of the weight of all ...
- 301
4
votes
1
answer
369
views
Are these visualisations the filters of the convolution layer or the convolved images with the filters?
There are several images related to convolutional networks on the Internet, an example of which I have given below
My question is: are these images the weights/filters of the convolution layer (the ...
- 111
2
votes
1
answer
488
views
Is it a good idea to use different width and height of the kernel in a CNN?
I always see that the width and height of the kernel are the same. But is it a good idea to use different numbers?
Recently I tried to use GoogLeNet (which expects images to be 224x224) on my images (...
user40943
4
votes
0
answers
80
views
Visualizing the Loss Landscape of Neural Nets: Meaning of the word 'filter'?
I found myself scratching my head when I read the following phrase in the paper Visualizing the Loss Landscape of Neural Nets:
To remove this scaling effect, we plot loss functions using filter-wise ...
- 41
2
votes
0
answers
34
views
Do filters have as many layers as the depth of the input in CNNs? [duplicate]
Firstly as an example here is the architecture of YOLOv2
I am trying to understand the depth of an output of a convolutional layer. For example, the first convolutional layer has the shape 3x3x32. So ...
- 63
2
votes
0
answers
163
views
How will the filter size affect the transpose convolution operation?
After a series of convolutions, I am up-sampling a compressed representation, I was curious what is the methodology I should follow to choose an optimum kernel size for up-sampling.
How will the ...
- 41
4
votes
1
answer
303
views
Is there anything that ensures that convolutional filters don't end up the same?
I trained a simple model to recognize handwritten numbers from the mnist dataset. Here it is:
...
- 723
1
vote
1
answer
2k
views
What is the difference between Attention Gate and CNN filters?
Attention models/gates are used to focus/pay attention to the important regions. According to this paper, the authors describe that a model with Attention Gate (AG) can be trained from scratch. Then ...
- 159
0
votes
1
answer
224
views
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 ...
- 31
2
votes
2
answers
1k
views
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 ...
- 3,491
1
vote
2
answers
454
views
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 ...
- 241
1
vote
2
answers
171
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:
...
- 143
3
votes
1
answer
668
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 ...
- 33
3
votes
0
answers
59
views
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 $5 \times 5$ ...
1
vote
3
answers
187
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 ...
- 11
4
votes
1
answer
242
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 ...
- 283
1
vote
0
answers
65
views
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. ...
- 568
2
votes
1
answer
393
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 ...
- 175
0
votes
1
answer
442
views
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 ...
- 175
8
votes
2
answers
561
views
What is the point of using 1D and 2D convolutions with a kernel size of 1 and 1x1 respectively?
I understand the gist of what convolutional neural 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 ...
- 81
0
votes
1
answer
1k
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 ...
- 1,240
6
votes
2
answers
1k
views
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 ...
- 193
6
votes
2
answers
8k
views
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 ...
- 195
1
vote
1
answer
892
views
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 ...
- 1,283
1
vote
1
answer
1k
views
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 ...
- 763
1
vote
1
answer
425
views
How can I make the kernels non-learnable and set them manually?
I'm a newbie in Convolutional Neural Networks. I have found out that kernels in convolutional layers are usually learned while training.
Suppose I have a kernel that is very good to extract the ...
- 493
2
votes
1
answer
4k
views
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: ...
- 157
2
votes
1
answer
62
views
What are some references that describe known filters (or kernels) and how we can create new ones?
I'm pursuing a master's degree in Artificial Intelligence. My final work is about Convolutional Neural Networks.
I was looking for information about filters (or kernels) of the convolutional layers. I ...
- 493
5
votes
1
answer
1k
views
How many weights does the max-pooling layer have?
How many weights does the max-pooling layer have?
For example, if there are 10 inputs, a pooling filter of size 2, stride 2, how many weights, including bias, does a max-pooling layer have?
- 53
5
votes
2
answers
669
views
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 ...
- 63
2
votes
1
answer
242
views
If I wanted to calculate multiple feature maps in a convolutional layer, should the filters be trained individually?
Assume I have an input of size $32 \times 32 \times 3$ and pass it to a convolution layer. Now, if my kernel size were to be $5 \times 5 \times 3$ and the depth of my convolution layer were to be 1, ...
- 158
4
votes
1
answer
959
views
How is the number of parameters reduced in the group convolution?
I think I don't understand group convolutions well.
Say you have 2 groups. This means that the number of parameters would be reduced in half. So, assuming you have an image and 100 channels, with a ...
- 225
4
votes
1
answer
388
views
Why is the number of output channels 16 in the hidden layer of this CNN?
In this tutorial from Jeremy Howard: What is torch.nn really? he has an example towards the end where he creates a CNN for MNIST. In nn.Conv2d, he makes the ...
- 183
63
votes
11
answers
55k
views
In a CNN, does each new filter have different weights for each input channel, or are the same weights of each filter used across input channels?
My understanding is that the convolutional layer of a convolutional neural network has four dimensions: ...
- 763
6
votes
1
answer
4k
views
How are the kernels initialized in a convolutional neural network?
I am currently learning about CNNs. I am confused about how filters (aka kernels) are initialized.
Suppose that we have a $3 \times 3$ kernel. How are the values of this filter initialized before ...
- 401
7
votes
2
answers
7k
views
How do we choose the kernel size depending on the problem?
Obviously, finding suitable hyper-parameters for a neural network is a complex task and problem or domain-specific. However, there should be at least some "rules" that hold most times for the size of ...
- 256
3
votes
3
answers
6k
views
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.
...
- 133