Questions tagged [convolution]

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What does it mean to say convolution implementation is based on GEMM (matrix multiply) or it is based on 1x1 kernels?

I have been trying to understand (but miserably failing) how convolutions on images (with height, width, channels) are implemented in software. I've heard people say their convolution implementation ...
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Understanding image size changes in DCGAN

I have been studying and trying to implement Generative Adversarial Networks using PyTorch. More precisely I tried to replicate the DCGAN PyTorch Tutorial tutorial using some custom dataset. My code ...
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Strategy for improving performance of 3D convolutional GAN

Others working with neural nets and GAN's might find this question interesting. Background: I've been working with data from Berkeleys PEER Ground Motion Database to generate new novel seismic traces. ...
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Backprop through Max Pool when the centre cell is of the maximum value [Edge Case]

In Backprop through a max pool layer, the gradients are translated back to that weight from which you got the maximum value during feedforward. You can read more about it here:Backprop Through Max-...
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Can I tune a model after training it? (Convolutional Neural Network & Classification)

I am relatively new to Data Science and I've recently embarked on a project. Long story short, I've trained a CNN model to distinguish between Male and Female genders. However, I wish to tune my model....
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How to claim that a CNN model is lightweight?

CNN model has some parameters that can show the a model is lightweight compared to others. The parameters can be Size(after training), Trainable parameters or multiply add. Which parameters are ...
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How to perform upsampling (and NOT interpolation) process theoretically modelled?

As an example, I know that sampling a signal $s$ is modelled by multiplication of s by a dirac comb, which has the effect of convolving the Fourier Transform (FT) of $s$ by the FT of the dirac comb ...
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How to Convolve a High-Res Image by a (fully convolutional) CNN kernel?

My CNN is an extremely simple neural network. ...
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How to backpropogate Convolution layer padding inputs with respect to output derivative

I created a convolution network with 5 Conv blocks, let discuss the issue based on Conv block 4 & 5 Conv Block 4 Input Image size : 28 * 28, Padding size 1 : 30 * 30 (image size ...
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PyTorchs ConvTranspose2d padding parameter

Im confused about what PyTorchs padding parameter does when using torch.nn.ConvTranspose2d. The docs say that: "The padding argument effectively adds dilation * (kernel_size - 1) - padding ...
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Why is the accuracy on the test dataset very low when training a neural network on an IMU dataset?

I am trying to train an IMU (Inertial Measurement Unit) dataset. The dataset contain 6 features (3-gyro, 3-accelerometer) and 1 label column. I have build a neural network via Conv1D, LSTM and Dense ...
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weight sharing among neurons at same depth

I'm trying to understand some visual illustrations about the wight sharing in the Convolutional Neural Network as following: In this picture we see that for different outputs different inputs share ...
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Up to which layer can we consider the encoder to be?

I'm trying to extract the encoder from a U-Net network. Given its architecture: And its summary: ...
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Few shot learning in object detection

I am working on a project to detect buildings from satellite imagery in Tanzania using convolutional neural nets. I use a pre-trained model which I further train on a selected area in Tanzania. The ...
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Understanding how convolutional layers work

After working with a CNN using Keras and the Mnist dataset for the well-know hand written digit recognition problem, I came up with some questions about how the convolutional layer work. I can ...
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How is it possible to upsample 2x with a 3x3 convolution?

From the Pytorch docs on Conv2Transpose2d, the formula to compute the output of the upsampled convolution (assuming square input and no kernel dilation) is: $$H_{out} = (H_{in} - 1) \times S - 2P_{in}+...
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Majority of feature maps of CNN are black

Assuming we have a following CNN : Conv->MaxPool->Conv->Maxpool->Linear. What does it mean - intuitively - if the majority of the feature maps of the first convolutional layer are black i....
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Convolutional neural network with 1 channel images/1 input channel

I'm following a tutorial on tensorflow using a convolutional neural network for images, but I'm looking to do it with grayscale images. How would the code posted there be different if it was for ...
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how to reconstruct image from feature space of convolutional neural network?

lets say i have fed an image into VGG19 pre-trained on imagenet as follows: ...
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Finding bounding box coordinates in Object Detection

In some of the OpenCV implementations for object detection , I don't understand how the co-ordinates of the bounding box of an object are extracted from the image. For example , In Object Detection ...
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Can anyone verify my NN diagram if it is properly drawn?

I am working on a Neural Network that can estimate building's carbon footprint based on the set of features and an image of urban surroundings (via CNN). I have used Netron to visualize the network (...
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Difference between convolution structures

I am having a hard time understanding the difference what is a multichannel CNN: In the paper titled, "A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data ...
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I'm trying to build a ResNet 18 model for Cifar 10 dataset, but I'm not able to fit the data dimension

At avergae pooling after the ConvNet, the error is displayed as the dimensions cannot be negative because the shape the previous output layer is 1,1,512 and on this the maxpooling cannot be done. Is ...
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Help required in understanding how the error of a convolutional layer is calculated when filter and delta of next layer have differing dimensions

I am trying to implement a CNN in NumPy so as to better understand its inner workings My architecture is as follows 10 images with 1 channel and with 28-pixel rows and columns (Dimension : (...
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How does “ Sparsity of connections” in CNNs causes the network to have less parameters?

I am studying Andrew NG's lectures on Convolutional Neural Network and he had provided two reasons for CNNs having less parameters compared to Non-Convolutional networks . They are : Parameter ...
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What does it mean when the shape of input images is (600,64,64,3)?

While attempting an assignment, I found that shape of the input image was (600,64,64,3). I thought 3 stood for the number of channels but it's listed as the 4th dimension. What does this mean? This is ...
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CNN Multi-Class Model Only Predicts 1 class for all test images

I am trying to build a CNN model to predict 42 classes. I used pre-trained models for this. I used Xception. This is how I have imported my dataset: ...
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Training a CNN on a large dataset

I am currently trying to build a CNN for around 100,000 images. There are 42 classes. I have used the default batch size of 32. This is how my model looks like: ...
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Does a CNN think things inside the filter are collocated aka dependent on each other?

I am running a 1D CNN on tabular data. The rows are data that I have are not sequential, that is to say they are not part of a time series or ordered string, which is why I am not using an LSTM. So ...
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What is an optimal local sparse structure of a convolutional vision network?

I was reading the InceptionNet Paper, where I found quite a few references to developing a sparse network structure, but I am not clear on what this means. An ...
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Image classification using cnn [closed]

I did image classification using CNN and it successfully classified the images but How to save predicted images to separate folder for example i have two classes cat and dog after prediction how to ...
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What's the best approach to classify labeled stockchart images , based on their curve

I'm working on an exercise where I have selected a lot of different stock chart images, showing the week closing of around 1200 companies at two different periode in time. I have stock charts from ...
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Is there a tool that interactively shows the receptive field of a feature map's element?

The receptive field of an element of a feature map is defined as the elements (and/or pixels) in the previous layers that define that element. I am looking for a tool that would allow me to select any ...
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How to make sense of the input and output for a speech-generation model like WaveNet?

I am currently studying this model speech generation known as WaveNet model by Google. https://arxiv.org/pdf/1609.03499.pdf using the linked original paper and this implementation. I find the model ...
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Custom convolution kernels for dot-product between features in a feature map

I am trying to implement Interaction and Aggregation block from this paper: paper, and I faced difficulties with SIA Module. As I understand the paper, I need to: get KxK patch for each (h, w) ...
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Why does this model print Incompatible dilated conv1d layers?

I was trying to see the layers used in a Wavenet model for speech generation and I can't seem to make sense of the output layers printed by the TF model. Model is this: https://github.com/Rayhane-...
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Extracting the features from several Auto Encoders

I am trying to extract the features of sparsed 3D pictures via a Convolutional Auto Encoder(CAE). Dou to the high computational costs I can not train the CAE over all the samples. I prepared couple of ...
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CNN model contains several images that are null

I'm using a deep CNN with the ReLU activation function. When visualizing the layers (each with 32 filters), several of the filtered images are zeros. I am trying to reason why this may be happening? ...
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best NN architecture for point prediction

I'm training to predict a single value y (continuos in [0,1]) based on a number of variables ...
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deep learning model gives same probabilities for the same class

I'm building a CNN neural network with Pytorch. Although the model accuracy is 85.6%, after getting image probabilities with torch.exp(output) and getting the top ...
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CNN Architecture comparison standards

I want to add comparison of accuracy section in my study report on CNN Architecture for a medical data. I have already added the comparison by VGG 16, AlexNet etc. Is it a standard to compare the ...
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Caps_Net. searching for example and library to use

Which library is most recommended and easy to use?
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CNN - multidimensional matrix as input or parallel input for parallel CNN

I want to run CNN on 20 channels of images. One way is to run on a 20-channel multidimensional matrix (like RGB ). Another way is to run 20 CNN on one channel at a time ( R apart from G separately ...
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How many computations in a CNN

I have not been able to find an answer, so if it is out there, please let me know. I would like to calculate the amount of time, that a uC needs to give me an Output of an CNN. Therefore, I would ...
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Should I apply 1D or 2D CNN on binary text classification?

I am trying to train a text classification model. For all sentence examples, I limit them up to 32 words, and if there are not exist 32 words, I am creating zero pad arrays. To convert each word to ...
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How to update filter weights in CNN by example?

I'm trying to understand how to update filters weights during backpropagation in CNN. Architecture: RGB Input image 5x5 Convolution (Stride = 1, no zero padding, filter size = 2x2 : [{1, -1},{0,0}]) ...
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using maxpool before 1x1 conv in inception module

there is a model described here (fig 3 page 52, also used in CNN course by Andrew ng) which used to build an inception model. for reducing the cost of convolutional layers, it used a 1x1 conv layer ...
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Implementing the SVHN CNN architecture in Srivastava et al. 2014 Dropout paper

I am trying the implement the CNN architecture introduced in Srivastava et al. 2014 Dropout paper (appendix B.2), for the SVHN dataset. I implemented only the convolutional layers part, without ...
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What is dropout in convolutional layers and how does that different from max-pooling-dropout?

When dropout is applied to fully connected layers some nodes will be randomly set to 0. It is unclear to me how dropout work with convolutional layers. If dropout is applied before the convolutions, ...
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How to determine the configuration of Conv2D layers?

I have this autoencoder with two parts:- encoder and decoder. However, I am having problem defining the configuration of these Conv2D layers. This is how my model look like:- input_img = Input(shape=(...

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