Questions tagged [convolutional-neural-networks]

For questions about convolutional neural networks, also known as CNN or ConvNet.

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How to implement Multiple Neural network architecture, connected in parallel and series in Keras or Pytorch

Hello Dear StackExchange members, I want to make a deep network as shown in the image. I want each 'network 1 to look at the specific part of the input and I don't want to divide my input beforehand ...
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Inverting intensity on images to enhance image dataset

i just tried to improve my image dataset by inverting the images with a probability of 50% (means white background, black features transforms to black background, white features) I thought this will ...
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319 views

Applying a 1D convolution for 4D input

i'm trying to implement this paper and I'm stuck for quite some time now. Here is the issue: I have a 3D tensor and has (180,200,20) as dimension and I'm trying ...
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25 views

What are the advantages of time-varying graph CNNs compared to fixed graph?

As I wrote in the title, what are the advantages of time-varying graph CNNs compared to fixed graph? For example, in CORA, which is a graph of citation relations of papers frequently used in graph CNN,...
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Binary annotations on large, heterogenous images

I'm working on a deep learning project and have encountered a problem. The images that I'm using are very large and extremely detailed. They also contain a huge amount of necessary visual information, ...
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60 views

Why is graph convolution network in time-varying graphs useful for anomaly detection?

In this paper, the authors refer to the application of time-varying graphs as an open problem. And they say it will be useful for anomaly detection in financial networks, etc. But why is that useful?
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51 views

Examples of time-varying graph-structured data in real world

I'm looking for examples of time-varying graph-structured data for time-varying graph CNNs. First, I came up with the idea of infection network. Is there anything more? If possible, I want data that ...
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How to voxelize multiple frames at the time and append them together?

I'm trying to implement this approach for object detection and tracking. In this approach, the first step is voxelize each frame to construct a 3D tensor, the second step is to append multiple voxels ...
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47 views

How is the bias caused by a max pooling layer overcome?

I have constructed a CNN that utilizes max-pooling layers. I have found with these layers that, should I remove them, my network performs ideally with every output and gradient at each layer having a ...
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What are some neural network models that can use auxiliary info during training for image segmentation?

What are some deep learning models that can use supplementary information other than RGB channels for image segmentation? For example imagine a poorly shot image of a river (blue) that shows a gap, ...
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What parameters can be tweaked to avoid a generator or discriminator loss collapsing to zero when training a DC-GAN?

Sometimes when I am training a DC-GAN on an image dataset, similar to the DC-GAN PyTorch example (https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html), either the Generator or ...
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How are exploding numbers in a forward pass of a CNN combated?

Take AlexNet for example: In this case, only the activation function ReLU is used. Due to the fact ReLU cannot be saturated, it instead explodes, like in the following example: Say I have a weight ...
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What are the differences between network analysis and geometric deep learning on graphs?

Both of them deal with data of graph structure like a network community. Is there a big difference there?
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Why can a fully convolutional network accept images of any size?

On this article, it says that: The UNET was developed by Olaf Ronneberger et al. for Bio Medical Image Segmentation. The architecture contains two paths. First path is the contraction path (...
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53 views

What is the purpose and benefit of applying CNN to a graph?

I'm new to the graph convolution network. I wonder what is the main purpose of applying data with graph structure to CNN?
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Keras CRNN implementation with multiple input images

Hello I am trying to implement a CRNN with multiple input images (in my context it is 6 images) This is a regression problem and output is two real value. And for the CNN block I am thinking of using ...
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2answers
242 views

How can I use 1-channel images as input to a CNN?

I need to develop a convolutional neural network whose inputs are 1-channel images, but I dont know how to do it, given that most libraries use 3 channel images. Should I convert my images to RGB? Is ...
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How High and Low frequency filters effect activation in the next layer?

Generally, we come across terms such as High Frequency and Low frequency filters in Convolutional Neural Networks (CNN). In regards to this highlighted statement, in 'S1' section of this paper by ...
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1answer
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Convolutional Neural Networks for different-sized Source and Target

CNNs are often used in one of the following scenarios: A known-sized image is encoded to an intermediate format for later use An intermediate or precursor format is decoded into a known-sized image ...
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Not clear about CoordConv

I read the CoordConv paper and I am a bit confused about its implementation for a GAN/VAE. I understand how to add 2 more channels to an image and pass that to a conv net (and there are good online ...
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146 views

Dense bottleneck layer in Autoencoder

I would like to use the bottleneck layer of U-Net (last layer of the encoder) to calculate the similarity between two images. For that I have to somehow flatten the last layer of the encoder. In my ...
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394 views

What is the use of softmax function in a CNN?

What is the use of softmax function? Why was it used at the end of fully connected layer in convolution neural network?
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1answer
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How does ARKit's Facial Tracking work?

iPhone X allows you to look at the TrueDepth camera and reports 52 facial blendshapes like how much your eye is opened, how much your jaw is opened, etc. If I want to do something similar with other ...
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52 views

Why does Convolutional layer unde usually has the same input/output channel size?

As famous model VGG16 shows(and other famous models), The convolutional layers before pooling usually have the same input and output channel sizes? What's the reason for that? Is there a theory or ...
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245 views

Autoencoder for MobileNetV2

I have way more unlabeled data than labeled data. Therefore I would like to train an Autoencoder using MobileNetV2 as the encoder. Then I will use the pretrained model for the classification of the ...
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1answer
85 views

Why should each filter have different weights for each input channel?

From the answers to this question In a CNN, does each new filter have different weights for each input channel, or are the same weights of each filter used across input channels?, I got the fact that ...
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Can GANs be used to generate matching pairs to inputs?

I have some limited experience with MLPs and CNNs. I am working on a project where I've used a CNN to classify "images" into two classes, 0 and 1. I say "images" as they are not actually images in the ...
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58 views

What is wrong with this CNN network, why are there hot pixels?

I'm building a CNN decoder, which mirrors (in reverse) the VGG network structure from Conv-4-1 layer. The net seems to be working fine, however, the output looks broken. Please note that the colour ...
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How do we give a kick start to the Facenet network?

I read the Facenet paper and one thing I am not sure about (it might be trivial and I missed it) is how do we give the kick start to the network. The embeddings in the beginning are random, so ...
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52 views

Convolutional neural network debugging

Im trying to implement CNN for small images classification (36x36x1) (grayscale). I've checked every forward/backward pass function on small example, and still my cnn is not doin any progress on ...
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45 views

Neural Nets: CNN confirming layer/filter arithmetic

I was hoping someone could just confirm some intuition about how convolutions work in convolutional neural networks. I have seen all of the tutorials on applying convolutional filters on an image, but ...
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Super Resolution on text documents

I want to implement super-resolution and deblurring on images from text documents. Which is the best approach? Are there any Git-hub links which will help me to start? I am new to the field. Any help ...
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243 views

How can I train a deep learning model to predict a matrix?

I am trying to train a deep learning model to predict an 8*2 matrix. The predicted matrix would have complex values and the input matrix would be real numbers. Can it be done? Thank you for your time.
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129 views

Can a CNN be trained incrementally?

Like our human brain, we can first learn (train) the handwriting 0 and 1. After the traing (and test) accuray is good enough, we only need to study (traing) the hardwriting 2, Instead of cleaning all ...
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Object size identification and maximum number of classes with convolutional neural networks

I am working on a project that involves using a ConvNet to identify screws. I am able to train from scratch a ConvNet based on the first version of the inception network, but shallower (only 3 ...
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Training a reinforcement learning model with multiple images

I am tentatively trying to train a deep reinforcement learning model the maze escaping task, and each time it takes one image as the input (e.g., a different "maze"). Suppose I have about $10K$ ...
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1answer
46 views

How to add some data input in a CNN?

There is this problem I have encountered, I was trying to classify the pixels from input image into classes, sort of like segmentation, using a encoder-decoder CNN. The “interested” pixels usually ...
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1answer
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Convolutional layer to Fully Connected Layer implementation

Im implementing a neural network framework from scratch in C++ as a learning exercise. There is one concept I don't see explained anywhere clearly: How do you go from your last convolutional/pooling/...
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Are filter kernels fixed or learned?

No matter what I google or what paper I read, I can't find an answer to my question. In a deep convolutional neural network, let's say AlexNet (Krizhevsky, 2012), filter weights are learned by means ...
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Learning utility function for AIS data

I am trying to learn utility functions for ships through their AIS data. I have a lot of data available and plan on focusing on fishing boats. So far I've researched a lot of IRL algorithms but I'm ...
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2k views

RTX 2060 vs GTX 1060 6GB for deep learning

First of all I don't play games at all and I still quite new to deep learning.I was using Alex-net(transfer learning actually) in MATLAB to classify images in my current laptop(i5-3230,without any GPU)...
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What are the value of the pixels of the convolved image?

I'm studying convolutional neural networks from the following article https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/. If we take a grayscale image, the value of the pixel will be ...
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How are filters weights updated for a CNN?

I've been trying to learn backpropagation for CNNs. I read several articles like this one and this one. They all say that to compute the gradients for the filters, you just do a convolution with the ...
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1answer
37 views

Convolution layer neurons when extracting multiple feature maps

I've recently been reading up on CNNs and this part of the architecture is really confusing me. Assume, I have an input of size [32*32*3] and pass it to a convolution layer. Now, if my kernel size ...
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86 views

How to manage large amounts of image data for training?

Right now, I am trying to synthesize training images for a CNN and due to the nature of the application, there is a finite number of sample images to learn from. From other research, I expect to be ...
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Is the number of feature maps equal to the number of kernels in the LeNet 5 architecture?

In LeNet 5's first layer, the number of feature maps is equal to the number of kernels. However, the second convolutional layer has a depth different from the 3rd layer. Does the filter size dictate ...
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Super resolution of an object in a video using adjacent frames

Video frames super-resolution with deep learning? I've been searching for the whole day and could find no papers\projects tackling that problem. For example: suppose I have a series of N frames of ...
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How to debug and find neurons that most influenced a pixel in the output image?

I'm building CNN network of Image to Image. After training, I have some bad results in part of the Image. I would like to find the neurons that most influenced those pixels and do retraining only ...
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111 views

Artifacts After pruning Unet CNN

Im trying to make a dark image brighter using CNN-UNet arcitecture. When I train the network I get the following results: When I cut the features in half for pruning, and do full train again, I get ...
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How would you feed a neural network a variable sized array as an input?

From what I've seen, neural networks take a set of atomic inputs. I want an input to be a variable array, i.e. a group of people (with unique IDs). If I didn't care about their ID, I could simply ...

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