# Questions tagged [convolution]

For questions related to the convolution operation in mathematics, convolutional neural networks, image processing and computer vision.

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### Understanding matching of a CNN Layer's Output With the Receptive Field of Input Layer

I was trying to implement the following paper: https://arxiv.org/abs/1610.01563 and I came across something that seemed ambiguous to me. On page 4, second paragraph, it says After processing the ...
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### How to calculate the gradient for the output with respect to the input pixels

Hi for my project I'm using a somewhat simple CNN consisting of several convolution layers and pooling layers. Essentially the model is trained to perform a blur of sorts on an input image. For my ...
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### What's the best criterion for evaluating activation maps in a CNN?

I'm currently studying CNNs and I had the idea of building a model without a fully connected layer at the end. I think this could be beneficial, if one can somehow model the desired outputs as a ...
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1 vote
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### What is the matrix representation of a dilated convolution?

I am delving into the matrix representation of dilated convolutions, especially after understanding standard 1D convolutions as Toeplitz matrix-vector multiplications. My specific focus is on Dilated ...
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### Why are convolutions and pooling described as layers in a network?

Whenever I look at resources on convolutions and max-pooling in CNNs they always seem to describe these algorithms as being part of the network - a preliminary set of layers before the main ...
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### How does Conv2Plus1D reduces the number of paramateres?

Based on this tutorial, and the An advantage of this approach is that factorizing the convolutions into spatial and temporal dimensions saves parameters. statement, the Conv2Plus1D must have fewer ...
1 vote
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### Do all CNNs learn to detect edges in the first layer?

I was looking at 3D CNNs that process volumetric data, e.g. for MRI images of brain, where the input is a 4D tensor, and I couldn't find images from the filters of the first layer. Suppose that ...
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### Is there a correct order of "conv2d", "batchnorm2d", "ReLU/LeakyReLU", "MaxPool2d" for UNet like architectures?

Context I'm investigating the UNet architecture for a little while now. After investigating the structure of the official UNet architecture as proposed in the official paper I noticed a recurrent ...
397 views

### 2D convolution with channels versus 3D convolution for layers of a map?

Introduction I am considering to use a convolutional neural network in implementing Monte Carlo control with function approximation. I am using a Monte Carlo estimate as it is unbiased and has nice ...
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1 vote
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### Can a convolution learn to generate fine details? [closed]

I'm trying to get a convolutional autoencoder to reconstruct images of a dataset with crisp details. I've read in a couple places that convolutional autoencoders "naturally produce blurry images&...
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### How Does Convolution Backpropagation Work?

Assume in a convolutional layer's forward pass we have a $10\times10\times3$ image and five $3\times3\times3$ kernels, then $(10\times10\times3) *( 3\times3\times3\times5)$ has the output of ...
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### Is there a best practice for creating multiple convolutional layers from small image inputs?

With all the work being done on larger and larger images, I'd like to ask if a best practice(s) has arisen for allowing multiple convolutional layers on small image inputs? For instance, in my case I ...
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### ML model giving rank errors on 3D layers on converting 2D images to 3D models

i am currently working on a model to convert 2d images to 3d models through a ml model. For this i have taken into reference a research paper which had this diagrammatical flow of layers & i have ...
1 vote
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### Combining Different Inputs in a Neural Network for Numerical Integration

I am building a NN that numerically integrates a non-linear differential equation. Given a DE: $$\frac{d}{dt}x(t) = f(x, p)$$ with solution $x \in \mathbb{R}^n$ and parameters $p \in \mathbb{R}^m$, ...
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### What are the benefits of using multiple convolutions, as opposed to one, before the pooling layer in a U-Net?

I have seen U-Nets that use a single convolution before the pooling operator and some that use two or more. My question is, what is better? Or what are the benefits of using more or less convolutions?
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### Implement 4D convolution as matrix-matrix multiplication - paper is confusing!

I am confused by this paper https://arxiv.org/pdf/1410.0759.pdf which displays on page 4 how to model a 3D convolution (input has more than 1 channel and filter has more than one output). In this ...
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### How are CNN kernels trained when using FFT for convolutions?

CNNs (convolutional neural networks) are adept at processing images, as their construction is based on the biological neural networks found in the human eye. "Kernels", sometimes called &...
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### Is down-sampling the only purpose of using stride?

Stride is used in at least two operations: convolution and pooling. Both operations can be viewed as applying a kernel function on input using a kernel (filter). Stride determines the amount of "...
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### What is a filter in the context of graph convolutional networks?

In Section 2.1 of the research paper titled Semi-Supervised Classification with Graph Convolutional Networks by Thomas N. Kipf et al., Spectral convolution on graphs defined as The multiplication of ...
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### In this paper, if region $R_{2}$ moves in a sliding window manner, won't the saliency map have a smaller size than the original image?

In the paper Salient Region Detection and Segmentation, I have a question pertaining to section 3 on the convolution-like operation being performed. I had already asked a few questions about the paper ...
318 views

### How do I choose the hyper-parameters for a model to detect different guitar chords?

I need to build a hand detector that recognizes the chord played by a hand on a guitar. I read this article Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation ...
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### Is it possible to apply 2D convolution to 1D data?

Suppose that I have a 1D dataset with 6 features. Can I apply a 2D convolutional neural net to this dataset?
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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 ...
1 vote
180 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, ...
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### When should we use separable convolution?

I was reading the "Deep Learning with Python" by François Chollet. He mentioned separable convolution as following This is equivalent to separating the learning of spatial features and the ...
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I have to build a model where I pre-process the data with a Gaussian kernel. The data are an $n\times n$ matrix (i.e one channel), but not an image, thus I can't refer to this matrix as an image and ...