Questions tagged [convolutional-neural-networks]
For questions about convolutional neural networks, also known as CNN or ConvNet.
394
questions
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1answer
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Why are activation functions independent layers in CNNs rather than part of convolutional layers?
I have been reading up on CNNs. One of the different confusing things has been that people always talk of normalization layers. A common normalization layer is a ReLU layer. But I never encountered an ...
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2answers
17 views
Are feature maps merged or are they passed on as they are?
I am unsure about the following parts of the architecture and mechanics of convolution layers in CNNs. Possibly, this is implementation-dependent though.
First question:
Say I have 2 convolution ...
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1answer
33 views
If the goal of training of a GAN is to have $P_g=P_{data}$, shouldn't this produce the exact same images?
Referring to the blog, Image Completion with Deep Learning in TensorFlow, it clearly says that we would want a generator $g$ whose modeled distribution fits our dataset $data$, in other words, $P_{...
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1answer
20 views
Understanding the reconstruction loss in the paper “Anomaly Detection using Deep Learning based Image Completion”
I would like to implement the approach represented in this paper. Here they used following reconstruction loss:
$$
L(X)= \frac{\lambda \cdot || M \odot (X - F(\overline{M} \odot X)) ||_{1} + (1 - \...
2
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1answer
40 views
Why do we normalize data in a deep neural network?
I have asked this question a number of times, but I always get confusing answers to this, like "normalized data works better", "data lives in the same scale"
How can ...
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0answers
7 views
Imbalance large dataset with keras using flow from directory
I want to detect dataset bias, and for that, the first approach is to build a model that can recognize from which dataset belongs an image.
I am working with Python3, with limited computing and more ...
2
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0answers
25 views
How can I incrementally train a Yolo model without catastrophic forgetting?
I have successfully trained a Yolo model to recognize k classes. Now I want to train by adding k+1 class to the pre-trained weights (k classes) without forgetting previous k classes. Ideally, I want ...
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1answer
35 views
Dropout causes too much noise for network to train
I am using dropout of different values to train my network. The problem is, dropout is contributing almost nothing to training, either causing so much noise the error never changes, or seemingly ...
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0answers
49 views
Can I perform image recognition using a very small dataset?
I have 3 types of classes and I have 2 images of each object (or class).
Can I perform image recognition using this very small dataset?
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0answers
18 views
Join Multiple Tensor from a CNN features extractor [migrated]
I have a mixed neural network. The first part is a CNN that extrapolate features from an image; the OUTPUT shape from this first part is
[None, 1, 1, 128]
...
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0answers
28 views
What to look for when CNN returns same prediction for every input?
I am trying to use a CNN to do a regression prediction on some statistical data. The data is time-series data formatted into a 2-D grid. The network I'm using looks like this:
...
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1answer
27 views
What is the best loss function for convolution neural network and autoencoder?
What is the best choice for loss function in Convolution Neural Network and in Autoencoder in particular - and why?
I understand that the MSE is probably not the best choice, because little ...
2
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1answer
46 views
What is the most common practice to apply batch normalization?
For a deep NN, should I generally apply batch normalization after each convolution layer? Or only after some of them? Which? Every 2nd, every 3rd, lowest, highest, etc.?
3
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1answer
121 views
Can a vanilla neural network theoretically achieve the same performance as CNN?
I perfectly understand that CNN takes into account the local dependency of each pixel to the nearby pixels. In addition, CNNs are spatially invariant which means that they are able to detect the same ...
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0answers
26 views
How to calculate size and offset of YOLO grid in a fully convolutional network with zero padding? [migrated]
Fully convolutional network with zero padding:
I have a fully convolutional network which does not have any padding in convolutional layers. This implies that, after each convolution operation, the ...
2
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0answers
45 views
Drawing a straight line through points where the tyre meets the ground
I followed these steps to write a program in MATLAB to detect the edge of the wheels where they meet the ground.
Read the image
k-means segmentation
Dynamic binarization
Dilatation and Erosion
ROI ...
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0answers
10 views
Is there a simple way of classifying images of size differing from the input of existing image classifiers?
Most image classifiers like Inception-v3 accept images of about size 299 x 299 x 3 as input. In this particular case, I cannot resize the image and lose resolution. Is there an easy solution of ...
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0answers
18 views
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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0answers
9 views
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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0answers
54 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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0answers
23 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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0answers
11 views
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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0answers
48 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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1answer
39 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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0answers
18 views
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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0answers
16 views
How is the bias caused by a max pooling layer overcome?
I have constructed a CNN that utilises 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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0answers
11 views
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, ...
3
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0answers
15 views
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 ...
3
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1answer
31 views
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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1answer
27 views
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?
2
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1answer
64 views
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 (...
2
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1answer
45 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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13 views
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
23 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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0answers
44 views
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
15 views
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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0answers
14 views
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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0answers
21 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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2answers
50 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?
2
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1answer
22 views
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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1answer
42 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 ...
0
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1answer
39 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 ...
1
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1answer
65 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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0answers
29 views
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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1answer
52 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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0answers
28 views
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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0answers
40 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 ...
2
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1answer
32 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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0answers
48 views
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 ...
0
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1answer
46 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.
