Questions tagged [convolutional-neural-network]

A convolutional neural network is a form of neural network with an additional convolutional layer, typically used in image & audio analysis. The convolutional layer is essentially a filtering stage defined by the kernel which is used. For example, a convolutional layer could have a kernel which extracts edges from an image towards the goal of learning which objects are in a scene.

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Fusion (concatenation or elementwise) of coarse (deep) and high-res (shallow) features in ResNet, FPN and UNet

I understand this functionality, but I've neither the intuition nor reasons why this works. I'm looking at three cases of this fusion: ResNet, UNet and Feature Pyramid Net (FPN). In ResNet, in each ...
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Cross Validation in Neural Networks

I am training a neural network and doing 10-fold cross validation to measure performance. I have read lots of documentation and forums telling that the set of weights that should be saved or ...
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Mathematical description of max pooling operation

As described in the paper "The all convolutional net", pooling can be described with the following formula (see image). With p → ∞ (maximum norm) it becomes the max pooling operation. Every ...
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CIFAR100 CNN does not learn

I am trying to train different CNN architectures on CIFAR100 and compare results with CIFAR10, with the same architecture (adjusting the predictive layer to manage the difference in the number of ...
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Working of Dense Layer

What kind of operation does Dense Layer perform to reduce dimemsion. So basically I have used Dense layer to compress the dimension all the time like from 10000 neurons to direct 2000 neurons or even ...
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Failed to synchronize: cudaErrorIllegalAddress: an illegal memory access was encountered [closed]

While running a code I am encountering this error any ideas on how to resolve this
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Loss function for a regression model in image segmentation task

I am training a model to segment an image to predict the degree of damage (ranging from 0: no damage, to 5: severe damage) for each pixel of an image. I have approached it this way: ...
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Number of parameters in CNN

I'm trying to understand the convolutional neural network and especially its parameters. I found several formulas on the internet, but I cannot understand them. For example: ...
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Sliding window over CNN feature maps for identification of characters in a grid

I am working on a project that requires me to identify characters in a fixed-sized grid. A possible solution would be to use a sliding window along with a CNN. This approach requires computing the ...
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Where is this Pytorch NN version of a Keras example wrong?

I want to write a network inverting the Radon transform. I found an example in Keras here. The network is given by: ...
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Understanding deconvolutional network loss function

In the paper (1), there is a description of a deconvolutional network. The loss function (with only one layer) compares the colour channels of the orignal image with the colour channels of the ...
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How do dilated convolutions used for upsampling the inputs in FCNs?

I am reading the paper (Long et al., 2015) on fully convolutional networks (FCNs), and I came across the section where the authors describe dilated convolution as the trick to compensate for the cost ...
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Graph convolution with global information

Is it possible to add global information in addition to node information using Spektral or Pythorch? For instance, information of nodes such as relative position and atomic mass give a molecular ...
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Making a row-wise convolutional layer in keras

I would like to make a layer that is almost exactly the same as a Conv2D layer, but essentially has a kernel for each row of the image, so that each row of the output is generated by taking the dot ...
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Pre-processing images for fine-tuning

When you are fine-tuning a CNN like ResNet, VGG, EfficientNet, etc and you want to train the model with your own images, or even when you want to do a inference with any image of your dataset, do you ...
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when depthwise separable convolution should be preferred over normal convolution?

As a novice in the realm of deep learning, I recently learned about Depthwise Separable Convolution. I have seen some tutorials and articles about it on internet, and in all of them the author ...
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Can a CNN have a different number of convolutional layers and kernel and what does it mean?

So if I have $3$ RGB channels, $6$ convolutional layers and $4$ kernels, does this mean that each kernel does a convolution on each channel and so the input for the next convolution will be $3 \times ...
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Convolutional Neural Network for Signal Modulation Classification

I recently posted another question and this question is the evolution of that one. By the way I will resume all the problem below, like if the previous question didn't ever exist. Problem description ...
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Why concatenating these layers, why applying masks over and over to partial convoluted image?

I have to ask some questions about one topic. In this sentence of Nvidia's article they are saying: "The last partial convolution layer’s input will contain the concatenation of the original ...
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Backpropagation derivation for a convolutional layer

I am trying to derive the backpropagation for a single convolutional layer (padding layer is being implemented separately, so no padding argument for the convolutional layer). This layer is given $\...
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No gradients provided for any variable, when using Lambda to round model output

I have a problem where I need to predict some integers from an image. The problem is that this includes some negative integers too. I have done some reasearch and came accross Poisson which does count ...
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How to improve my deep LSTM model for time series?

I want to train a deep model for my time series power consumption dataset. I have created a model consist of CNN, BILSTM, Encoder-Decoder, and dense layers. here is my model: ...
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Why does my validation loss increase, but validation accuracy perfectly matches training accuracy?

I am building a simple 1D convolutional neural network in Keras. Here is the model: ...
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Is it possible to design FCNN with image input and coordinate vector output?

So, the neural network I want to build should find objects on videogame footage. I'm planning to use fully convolutional neural networks as image dimensions may vary. As input I have image taken in-...
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Why keras Conv2D makes convolution over volume?

I have a very basic question, but I couldn't get the idea about 2D convolution in Keras. If I would create a model like this : ...
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Tensorflow parameters for CNN

I created the below simple model (taken from a Coursera course). It has a total of five convolutions. ...
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280 views

1D target tensor expected, multi-target not supported

I am trying to train my model. My model outputs a [4,2] tensor where 4 is the batch size and 2 because of binary classification. After receiving the outputs I found the index of the maximum element ...
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Tensorflow convolutional neural network error during training

I built a simple CNN for binary image classification (cat/dog). ...
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Why is $2^n$ so important in deep learning?

While initializing and training a deep learning model we often use some quantities such as number of hidden neurons in dense neural networks, number of filter in CNNs, batch_size while training a ...
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How to custom conv2D layer Keras using calculated values

This is my first question, Hello World I guess. I need to create a conv2D custom layer (at least, I think so), which should use my custom module for extracting values in the first layer. It would be ...
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Why would the accuracy of a model change when the loss doesn't?

I've trained 8 models based on the same architecture (convolutional neural network), and each uses a different data augmentation method. The accuracy of the models fluctuates greatly while the loss ...
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CNN accuracy depends on the location of an object on an image

I've trained several models to accept 352 (height) x 352 (width) x 3 (channels) input of an image to segment images. I then took the test set and scanned each patch of the image ...
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How to check for vanishing gradient in CNN?

Is there is a way to check for vanishing gradient for CNNs using Keras? Like, for example, drawing the weight distributions for each layer and seeing if they are vanishing to zero?
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Activation Function

I am very new to machine learning and made an experiment myself. I have a few questions: Can I use $Y = sin(x)$ or $Y = 2x$ as an activation function for a neural network? Is it necessary to increase ...
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Spliting Training Test and Validation for Image Dataset

I have 600 images in the training folder, 200 images in the validation folder, and 200 images in the test folder. Suppose if I fit the training data generator and validation data generator for some ...
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How to pass variable length data as feature to a neural network?

I am working on building a model to classify the type of touch the user makes (Long Press, Left Swipe, Right swipe, and so on). I have data with features that characterize the user's touch, like ...
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Problem with CNN [closed]

I am using the BreakHis database. More specifically, I am trying to classify the 400X images. The sizes of the images are $700x460x3$. Here are the details of the dataset. Also, here is the code for ...
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71 views

Understanding Conv1D Output Shape

I am a little confused with the output shape that Conv1D produces. Consider the code I have used as the following (a lot has been omitted for clarity): ...
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1answer
71 views

Calculate importance of input data bands for CNN image classification?

I constructed and trained a convolutional neural network using Keras in R with the TensorFlow backend. I feed the network with multispectral images for a simple image classification. Is there some way ...
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Inverting a matrix using a convolutional neural network

Just for a fun exercise, I am trying to invert a matrix, say size 28x28 (or even 5x5) with a neural network. The way I approached this (quite naively) is as follows: I built a fully convolutional ...
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How to modify this training function in order to print the aggregation of models

I have 3 VGG: VGGA, VGGB and VGG*, trained with the following training function: ...
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3answers
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Do I need to manually trim 300 videos?

I wish to train a model that detects the breed of a dog based on video input. I have a dataset containing 10 classes with 30 videos in each class. The problem is that for each of these videos, the dog ...
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Re-train a neural network when a new category is introduced

Say I have a dataset with 4 classes ["dog","cat","mouse","monkey"] and I train a CNN on a dataset to classify those classes. ...
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How Are Kernel Weights Trained in 1-D CNN's with Multi-dimensional Input?

I have far from a perfect understanding of how 1-D convolution neural networks learn, but I think I understand how the kernel operates on 1-D input data. How does 1-D convolution work with multi-...
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How to calculate Efficientnet's compound scaling

I would like to use compound scaling to tweak my own model, but I am confused about how to utilize the $d=\alpha^\phi,w=\beta^\phi,r=\gamma^\phi$ in compound scaling and how to compute the specified ...
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1answer
41 views

Autoencoder not learning walk forward image transformation

I have a series of 15 frames with (60 rows x 50 columns). Over the course of those 15 frames, the moon moves from the top left to the bottom right. Data = https://github.com/aiqc/AIQC/tree/main/...
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80 views

Is it possible to use a Neural Network to interpolate data?

I am completely new to Artificial intelligence and Neural Networks. I am currently working on a plasma physics simulation project which requires a very high resolution data set. We currently have the ...

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