Questions tagged [convolution]

Question 1

I have a fairly good understanding of the basic convolution operation and how padding & stride affect its output, but dilation is something new to me. So I am going to make some broad statements ...

Question 2

I have implemented a simple version of the kernel-based convolution operation as shown below. However, for faster code operation, I think the nested for loop is slowing up the operation. Is there any ...

Question 3

I was reading following paper: Deconvolution and Checkerboard Artifacts. The text says that Both deconvolution and the different resize-convolution approaches are linear operations, and can be ...

Question 4

Group convolutions theoretically should reduce the number of parameters and hence improve the speed of inference, without significantly affecting the performance of the model. However, I don't notice ...

Question 5

I have a question about how a CONV2D layer handles time series data. How with filters that scroll through time, our model can extract features and capture and model our target value? Thank you in ...

Question 6

Is the calculated output correct?

Question 7

Is there a set of rules or guidelines for designing filters for convolutional neural networks? For example, a 3 x 3 layer with ones in the first column, zeroes in the second, and negative ones in the ...

Question 8

If I have a convolutional layer with dimension (5,5,4), (i.e, 4 no. of 5x5x1 feature maps), what will be the dimension of the flattened layer, if I apply flattening ...

Question 9

I'm really a novice working with these technologies and I'm struggling to design a neural network that is powerful enough to model a spectrogram. For a personal project, I'm working on a spectrogram ...

Question 10

I was going through the introductory guide to convolutional neural networks in tensor flow here And I was trying to logically map some of the code I saw to my actual understanding of how convolutional ...

Question 11

Suppose I have a sequence data of size $B \times N \times d$ where $B$ is the batch size, $N$ is the sequence length, and $d$ is the dimension or the number of features. Suppose I want to do 1D ...

Question 12

I have been learning GAN (Generative Adversarial Networks) lately and having a hard time understanding the output size for transpose convolution. Let's say I am using a Tensor of [1, 64, 1, 1] as an ...

Question 13

I have a problem. I have a CNN model which is used for an NLP problem. This is written in Python. I have questions about this, which I can't find an answer to. Why is ReLu used inside the Conv1D ...

Question 14

Convolution extracts high-level features, but what about Transpose Convolution (or De/Up-Convolution)? Does it behave exactly the opposite? Does it generate lower-level features?

Question 15

I am studying a model where landmarks from an image are calculated. The work comes from Convolutional Experts Constrained Local Model for 3D Facial Landmark Detection. I need to confirm why the ...

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Questions tagged [convolution]

Convolution Kernels - Understanding dilation

Is there a more efficient implementation of kernel-based filtering?

Deconvolution, NN-resize convolution

Are group convolutions still used?

Conv2d with time series

Output of a convolutional layer

What are the general rules or principles for finding matrix operations that are used as filters in convolutional neural networks?

Shape of Flattened Layer in CNN

What neural network architecture would help me model a spectrogram?

How to align the description of a convolutional neural network in keras with wikipedia's conceptual model?

Multi-channel convolution in Tensorflow

Transpose Convolution Output Size

CNN model why is ReLu used in Conv1D layer and in the first Dense Layer?

Transpose Convolution feature extraction

why layer of dimension 1 is outputting image of size n

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