Questions tagged [convolution]

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

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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 ...
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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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Why does $I_N + D^{-\frac{1}{2}}AD^{-\frac{1}{2}}$ have eigenvalues in the range [0, 2]

In Semi-supervised classification with Graph Convolutional Networks, I am unable to understand few things. Given an undirected graph having adjacency matrix $A$, degree matrix $D_{ii} = \sum_j A_{ij}$ ...
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Attention vs Global Depthwise convolution

The main advantages of the self-attention mechanism are: Ability to capture long-range dependencies Ease to parallelize on GPU or TPU However, I wonder, why the same goals cannot be achieved by ...
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Why do we lose detail of an image as we go deeper into a ConvNet?

I was reading this research paper titled 'Image Style Transfer using Convolutional Neural Networks' which as the title suggests was based on Neural Style Transfer. I came across this line which didn't ...
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56 views

Why do we add 1 in the formula to calculate the shape of the output of the convolution?

In the formula to calculate output shape of tensor after convolution operation $$ W_2 = (W_1-F+2P)/S + 1, $$ where: $W_2$ is the output shape of the tensor $W_1$ is the input shape $F$ is the filter ...
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Dose the input channels of depth-wise convolution must be same with output channels?

Just learned the concept of group convolution and depth-wise convolution, I am confused what the difference between depth-wise and group convolution is. Does the input channels of depth-wise ...
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Is a convolutional layer capable of converting, for example, a binary image into an RGBA image?

I am asking this question for a better understanding of the concept of channels in images. I am aware that a convolutional layer generates feature maps from a given image. We can adjust the size of ...
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Is there any animation that illustrates the "fold" and "unfold" operations of convolutional layers?

There are fourteen convolution layers in PyTorch. Among them six are related to convolution, another six are related to transposed convolution. The remaining two are fold and unfold operations. The ...
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What does 'input planes' mean in the phrase 'input signal/image composed of several input planes'?

PyTorch documentation provided the following descriptions to the Convolution layers ...
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What does 'channel' mean in the case of an 1D convolution?

While reading about 1D-convolution in PyTorch, I encountered the concept of channels ...
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How is the convolution operation connected to neural networks?

I've been reading up on the convolution operation and neural networks. I understand that the convolution operation is defined as: $$(f * g)(t)=\int_{-\infty}^{\infty} f(\tau) g(t-\tau) d \tau$$ The ...
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More channels vs multiple inputs in neural network

Suppose I want to train the model for playing chess. I found that existing models use as input the grid with dimensions 8x8x20 (so we have 20 channels). Some channels may represent how different kind ...
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Positional encoding in convolutional layers

Positional encoding (PE) is an essential part of the self-attention layers in the transformer architectures since without adding it in some way (fixed of learnable) to the input embeddings model has ...
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How to implement a (3 + 2)-dimensional convolutional layer where the 2d space is "internal"?

I am trying to train a CNN to learn 5D (kind of) data. The data is structured as follows. It has three spatial dimensions [x, y, z], but it also has two "...
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Why the non-exploitation of edge labels in current graph convolutions "results in an overly homogeneous view of local graph neighborhoods"?

I am currently reading a paper called Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (2017, CPPR), and I cannot understand the following sentence: We identify that the ...
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Convolutional Layer Multichannel Backpropagation Implementation

I have been working on coding a CNN in python from scratch using numpy as a semester project and I think I have successfully implemented it up to backpropagation in the MaxPool Layers. However, my ...
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What is the difference between same convolution and full convolution in terms of feature map size?

In valid convolution, the size of the output shrinks at each layer. So after some point of time additional layers cannot meaningfully performs convolution. For this reason, same convolution is ...
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Why might the convolution be inappropriate when the task involves incorporating information from very distant locations in the input?

When I am reading about convolutional neural networks, I have encountered the following sentence from the textbook(page 341) that says about the limitation of the usage of the convolution in CNNs. ...
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Which layer to route out of layers of same width and height in Yolo implementation?

In Yolo configuration files (like yolo3.cfg in dark-net), there are many layers with output of same height and width due to ...
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What is meant by "real-valued argument" in this context of the convolution operation?

Consider the following statement from Deep Learning book (p. 327, chapter 9: Convolutional Networks) In its most general form, convolution is an operation on two functions of a real-valued argument. ...
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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 (...
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What do the variables in the cross-correlation formula mean?

I understand what cross-correlation does given a kernel and an input image, but the formula confuses me a little. Given here in Goodfellow's Deep Learning (page 329), I can't quite understand what $m$ ...
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Conversion of convolution filter weights between NCHW and NHWC formats

Popular deep learning frameworks have different default data formats (PyTorch with NCHW, TensorFlow with NHWC), which lead to published pretrained models with weights that may have an incompatible ...
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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 ...
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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 ...
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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: ...
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Is average pooling equivalent to a strided convolution with a specific constant kernel?

It seems to me that average pooling can be replaced by a strided convolution with a constant kernel. For instance, a 3x3 pooling would be equivalent to a strided convolution (of stride $3$) with a $3 \...
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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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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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381 views

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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How to mathematically describe the convolution operation (with a Gaussian kernel)?

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 ...
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Is it possible to express attention as a Fourier convolution?

Convolutions can be expressed as a matrix-multiplication (see e.g. this post) and as an element-wise multiplication using the Fourier domain (https://en.wikipedia.org/wiki/Convolution_theorem). ...
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How could I convolve a 4D image and a 4D filter with stride?

I want to create a CNN in Python, specifically, only with NumPy, if possible. For optimizing the time of convolution (actually correlation) in the network, I wanna try to use FFT-based convolution. ...
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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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Embedding Layer into Convolution Layer

I'm looking to encode PDF documents for deep learning such that an image representation of the PDF refers to word embeddings instead of graphic data So I've indexed a relatively small vocabulary (88 ...
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Why can we perform graph convolution using the standard 2d convolution with $1 \times \Gamma$ kernels?

Recently I was reading this paper Skeleton Based Action RecognitionUsing Spatio Temporal Graph Convolution. In this paper, the authors claim (below equation (\ref{9})) that we can perform graph ...
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Can I think graph convolution as 2D convolution like images?

Kipf et al described in his paper that we can write graph convolution operation like this: $$H_{t+1} = AH_tW_t$$ where, $A$ is the normalized adjacency matrix, $H_t$ is the embedded representation of ...
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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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Is it possible to apply the associative property of the convolution operation when it is followed by a non-linearity?

The associative property of multidimensional discrete convolution says that: $$Y=(x \circledast h_1) \circledast h_2=x\circledast(h_1\circledast h_2)$$ where $h_1$ and $h_2$ are the filters and $x$ is ...
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How will the input be preserved as we go deeper in CNN, where dimensions decrease drastically?

Our length of feature representation decreases as we go deeper into the CNN, I mean to say that horizontal and vertical lengths decrease while depth(channels) increase. So, how will the input be ...
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Do convolutional neural networks perform convolution or cross-correlation?

Typically, people say that convolutional neural networks (CNN) perform the convolution operation, hence their name. However, some people have also said that a CNN actually performs the cross-...
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Is there a difference between using 1d conv layers and 2d conv layers with kernel with size of 1 along other than time dimension?

Let's assume I use convolutional networks for time-series prediction. Data I feed to the network have 1 channel depth, height of number of periods and number of features is the width, so the frame ...
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How do we choose the filters for the convolutional layer of a convolution neural network?

Since the hidden layers of a CNN work as a trainable feature extractor, more detailed content based on a larger number of pixels shall require bigger filter sizes. But for cases where localized ...
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Are activation functions applied to feature maps?

If I have a convolutional neural network, and I convolve my input tensor with a kernel, the output is a feature map. Is an activation function then applied to this feature map? If its an image that ...
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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 ...
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How is the convolution layer is usually implemented in practice?

Following an earlier question, I'm interested in understanding the basics of Conv2d and especially how the kernel is applied, summed, and the propagated. I ...
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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 ...
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Is it a sign of overfitting when validation_loss dips and then goes up with increasingly bigger swings?

I am experimenting with a ConvNet to categorize images taken with a depth camera. So far I have 4 sets of 15 images each. So 4 labels. The original images are 680x880 16-bit grayscale. They are scaled ...