Questions tagged [convolutional-layers]
For questions related to convolutional layers, which are layers that perform the convolution (or cross-correlation) operation.
20
questions
1
vote
0answers
29 views
Error in MobileNet V1 Architecture?
From the architecture table of the first MobileNet paper, a depthwise convolution with stride 2 and an input of 7x7x1024 is followed by a pointwise convolution with the same input dimensions, 7x7x1024....
1
vote
1answer
51 views
How do you pass the image from one convolutional layer to another in a CNN?
I am currently trying to write a CNN from scratch, but I don't understand how to feed the information from a max-pooling layer to the next convolutional layer. Specifically, I don't know what to do ...
3
votes
1answer
61 views
What is the use of the regular convolutional layer in expansion path of U-Net?
I was going through the paper on U-Net. U-net consists of a contracting path followed by an expanding path. Both the paths use a regular convolutional layer. I understand the use of convolutional ...
2
votes
0answers
27 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 ...
0
votes
0answers
15 views
Add Additional Positional Information to Image Classification Neural Network
I am trying to find the best way to provide a neural network with both an image and some annotations about the image. Specifically, I'm creating a network to calculate an approximate 'cost' to go from ...
1
vote
0answers
28 views
Is the 3d convolution associative given that it can be represented as matrix multiplication?
I'm trying to understand if a 3D convolution of the sort performed in a convolutional layer of a CNN is associative. Specifically, is the following true:
$$
X \otimes(W \cdot Q)=(X \otimes W) \cdot Q,
...
2
votes
2answers
327 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 ...
2
votes
3answers
68 views
DQN not learning and step not stepping towards target
I am trying to create a simple Deep Q-Network with 2d convolutional layers.
I can't figure out what I am doing wrong, and the only thing I can see that doesn't seem right is when I get the model ...
2
votes
1answer
75 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 ...
1
vote
2answers
136 views
Is the stride applied both in the horizontal and vertical directions in convolutional neural networks?
In the convolutional layer for CNNs, when you specify the stride of a filter, typical notes show some examples of this but only for the horizontal panning. Is this same stride applied for the vertical ...
3
votes
0answers
34 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 $5 \times 5$ ...
2
votes
1answer
53 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 ...
1
vote
0answers
64 views
How can the FCNN reduce the dimensions of the input from $1048 \times 100$ to $523 \times 100$ with max-pooling?
I am trying to implement a paper on Image tempering detection and localization, the paper is Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks, I was ...
1
vote
0answers
39 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. ...
1
vote
1answer
65 views
Can fully connected layers be used for feature detection?
I need help in understanding something basic.
In this video, Andrew Ng says, essentially, that convolutional layers are better than fully connected (FC) layers because they use fewer parameters.
But I'...
1
vote
0answers
64 views
When to use convolutional layers as opposed to fully connected layers?
I am still new to CNNs, but I would like to check my understanding between when to use convolutional layers versus fully connected layers.
From what I have read, we can use convolutional layers with ...
7
votes
1answer
142 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 ...
2
votes
1answer
141 views
How is the depth of filters of hidden layers determined?
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 ...
1
vote
0answers
23 views
Wasserstein GAN with non-negative weights in the critic
I want to train a WGAN where the convolution layers in the critic are only allowed to have non-negative weights (for a technical reason). The biases, nonetheless, can take both +/- values. There is no ...
2
votes
1answer
135 views
Why do the inputs and outputs of a convolutional layer usually have the same depth?
Here's the famous VGG-16 model.
Do the inputs and outputs of a convolutional layer, before pooling, usually have the same depth? What's the reason for that?
Is there a theory or paper trying to ...
