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Questions tagged [conv-neural-network]

Convolutional Neural Networks are a type of neural network in which only subsets of possible connections between layers exist to create overlapping regions. They are commonly used for visual tasks.

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How do I import a bunch of images to use as input to a CNN that predicts multiple continuous variables?

I am trying to develop a CNN (in Python) that predicts multiple continuous variables, and am having trouble importing the images in a format that is acceptable as input to a CNN. I can't seem to find ...
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why the accuracy of my CNN decresing after some epochs?

at high accuracy, after some epochs the accuracy as well as validation accuracy is decreasing and got stuck after few more epochs. i dont understand why this happened. does more epochs at some point ...
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1D Convolution Maths

I would like to understand the maths behind 1d Convolution networks, as I'm getting errors along the lines of ...
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CNN Feature Normalization: Looking into the Future?

It occurs to me as I'm normalizing the features for my CNN that I am inadvertently taking the "future" into account by normalizing using the min and max of the entire time period. In other words, at ...
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Target for a Poker game Neural Network

I've created a python simulation of a texas holdem poker game with 3 random actions, check/fold, check/call and bet. I have then looped this round several times to produce a training data set for each ...
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Convolution neural networks vs Capsule networks

My question is more theoretical. Let's say I have 5x11 matrices where each row corresponds to some summary statistic. And each column refers to some subwindow of a large window. The matrix consists ...
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Training loss does not decrease beneath a very specific value

I am training a U-Net for the semantic segmentation of images. I have 3 classes (circles, squares, background) that I want to distinguish. No matter what changes I do to my network architecture, or ...
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Multiclass Segmentation Using U-Net: My training loss is not decreasing after certain epoch (accuracy not increasing) [duplicate]

So the problem is to perform a multiclass segmentation (255 classes of crops), and I am using a U-Net model for that. The input images are grayscale and the images of dimensions (128,128,1) are ...
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Which layer in a CNN able to detect spinned and translated objects

Conv layer or max pooling layer or anything else does the job? In my opinion, Conv layer or max pooling layer are able to do the job only when the rotations or translations are not too big.
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what exactly happens during each epoch in neural network training

1.Across different epochs, which of the following is/are updated? initial weights (initial ConvNet filter matrices, initial fully connected weights) hyper parameters: number of ConvNet filters, size ...
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How to detect if model is overfitting?

I know this question is asked billion times, but I could not really find an answer to my situation. So, I want to show all the logs of Keras model learning. The problem is I don't know if my model is ...
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on the understanding of visualization of the fully connected layer

When the final fully connected layer is visualized, it shows a big picture consisting of mini-pictures patching together and overlapping with each other. The mini-pictures are visibly recognizable to ...
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Mask RCNN for accidents

I've been using this implementation of mask rcnn : https://github.com/matterport/Mask_RCNN and I've been trying to figure out a way to detect accidents if the masks of two vehicles intersect at some ...
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Input Coordinates of Output Predictions of VGG16 Converted to FCN

The VGG16 classification network (CNN with fully connected layers) can be converted to a FCN (Fully Convolutional Network) by converting the fully connected layers to convolutional layers. In ...
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Is Multi-output Multi-Label classification possible for this merging problem?

I have programmed a game called cell wars where cells can capture other cells via merging. The image below shows the same board before merging (top) and after merging (below). Basically, overlapping ...
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What is the number of filter when using CNN for sentence classification

I am new to machine learning and NLP. During reading convolutional neural networks for sentence classification I'm having trouble understanding it. In the paper it says that a feature map c has ...
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What effect does 1x1 convolution has on quality of feature being learnt?

I was learning about inception module from deeplearning.ai by Andrew Ng, wherein we use 1x1 convolution to reduce computational cost. for example, if we directly apply 5x5 convolution we need to ...
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Validation loss increases while Training loss decrease

I am training a model and the accuracy increases in both the training and validation sets. I am using a pre-trained model as my dataset is very small. I am not sure why the loss increases in the ...
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Why does each convolution layer require activation function and weight initialization?

From a course on convolutional neural network, my understanding is basically that the convolutional layer does a convolution with a filter across your image, and generates some output (and maybe a ...
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this question is related to activation function

I could not understand : what is activation score from the kernel in the previous stage? I know what activation function mean ,activation functions type and how its work But what about activation ...
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CNN: correct way of reducing dimensionality of last feature maps

I want to reduce the features of the last convolutional layer of my CNN before connecting it to a dense layer to minimize the risk of overfitting. Lets say the feature maps of the last layer have the ...
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1answer
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Per-pixel classification using deep learning models

I want to train a model to classify image pixels in which neighbouring pixels are not considered, only channels (bands) for each pixel. I'm thinking about defining a CNN model which stacks several ...
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Weighted binary crossentropy in U-Net has no effect on accuracy (dice coefficient)

I am currently working on implementing a weighted binary crossentropy loss function as described in the U-Net paper ...
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1answer
19 views

Does sorting channels in multivariate time series affect performance of CNN?

I am now learning about CNN in machine learning. Also, I am trying to apply my knowledge to my another project which involves sensors attached on the body. There are some accelerometers and gyroscopes ...
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image dataset for Comparison of SVM and CNN

I'm looking for a suitable image dataset to train an SVM, a CNN and possibly an MLP as classifiers and to compare the results. Since an SVM archieves good results with small data sets and a CNN and ...
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Resize images disregarding aspect ratio for input to a CNN

I have a dataset of skin lesion images of varying non-square dimensions (e.g. 512x245, 842x98, 432x124) which I must resize in some way to consistent dimensions (e.g. 224x224) for input to a CNN. Is ...
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Huge difference between training/testing accuracies

I'm working in a kind of a sentiment classification (binary) task. Using google's pre-trained word2vec vectors for the embedding layer (tried other word vectors as well) and 2 Convolutional layers for ...
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Stereo image classification [closed]

I just been wondering how can I combine stereo vision and Convolutional neural networks for a classification problem
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Training convolutional neural network

I implemented Convolutional Neural Network from scratch for image recognition for 5 classes. When I train it using only one image from one class it seems to be working, because accuracy for this class ...
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1answer
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Higher value of strides in conv1d

I am using Conv1d for time-series data and I have create a model as follows, ...
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1answer
22 views

What is the definition of a “rectified conv feature map” in a convolutional neural network mentioned in the paper of “visual explaination”?

I have read the answer of the question What is the definition of a “feature map” (aka “activation map”) in a convolutional neural network? But I don't think that it is same as what I want. I ...
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How to approach node/graph classification in an event?

I'm facing a new project and thought about maybe going in the direction of Graph-Neural-Networks. My data comes in the form of events (unrelated to each other), the data in each events contains a 2D/...
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1answer
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Dimensions and implementation of the Convolution step in CNN

I am trying to write my own convolutional neural network from scratch (Python) and after reading several articles and watching tutorials (on CNN) there are still a couple of issues that I am unable to ...
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Will merging class labels in pre-trained model hurt transfer learning?

I'm using a large image dataset labeled with 15 classes to train a ConvNet model. The resulting model will then be used to enable transfer learning in a tiny dataset labeled with only 3 classes. The 3 ...
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Detecting the bounding box of an object in an image?

I have a dataset of images . Each image has an object in it. In a seperate csv file , I have been provided with the coordinates of the bounding box for the object in the training images. How do I ...
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CNN: Relationship between kernel size and node size in convolution layer

I have a question and that is maybe because I have a misunderstanding. In CNN, the convolution layer uses different filters (kernels). Let's imagine I have a network with 81 nodes in the input layer (...
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1answer
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How are convolution layer/max-pool layer operations carried out

I understand the concept behind why convolution layers / max pool operations work, but I cannot conceptualize how they are applied in typical neural network model. For example if I had a NN model ...
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1answer
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How do ConvNets self-organise to have a hierarchical segmentation of higher- and lower-level features?

As far as I know, each layer of a convolutional neural network used for image classification specializes in recognizing a different part of an image. At earlier stages in the network, more rudimentary ...
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How to train CNN for noise removal from images using Matlab [closed]

I am currently working on a project of mine where I want to use Convolutional neural networks for noise removal from images. I am talking about removing Poisson type of noise. The software that I am ...
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How to use the mean of a pre-trained CNN?

I have some doubts on how to use the mean of a pre-trained CNN. For example, if I use VGG-16 net, before the classification of an image I have to subtract the mean of the dataset (ImageNet in this ...
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How to correctly split data in training, validation and testing sets when some data is correlated

I have 400 data sets which each in turn have two to three images and there respective segmentations. The images in a given dataset are correlated to each other as they are segmenting the same object ...
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Multiple filters during backpropagation in convolutional neural network

Let's say we have an Input 10x10x3 (WxHxD) and 5 filters 3x3x3. Convolution between Input and filters will be 8x8x5. During backpropagation we will get error with the same size 8x8x5. While ...
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Can a trained neural network recognize rotating characters? [duplicate]

Suppose I have a trained neural network that can recognize, for example numbers from 1 to 10, the size of the picture $28 \times 28$. I made the rotation of these pictures by 90 degrees. Does now ...
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How to use MRI data as an input for a CNN?

I'm trying to train a convolution network for segmenting biomedical images U-net to segment parts of a magnetic resonance image (MRI) reconstruction; a 3D stack of 2D slices. What is the best way to ...
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2answers
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Am I missing obvious problems with my model [closed]

I am using Keras to train a CNN for a single label image classification. The model is being trained on synthesized data and applied to real world images. After a significant amount of trial and error ...
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1answer
42 views

What does a “similar” dataset mean in the context of fine tuning a CNN?

In https://arxiv.org/pdf/1809.09529.pdf it is said If the new dataset is similar to original dataset, we expect higher-level features in the CNN to be relevant to this dataset. Thus, it is ...
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1answer
31 views

Data augmentation methods for Raman Spectra

I'm building a CNN model based on Raman spectroscopy data and I wanted to experiment with data augmentation. What would be some reasonable techniques to try? I have found this paper which suggests ...
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1answer
36 views

How much data is needed to train CNN from scratch?

Any rule of thumb, on how many input images would be needed to have a reasonable chance not to overfit the data when training a CNN from scratch? In other words, what is a reasonable amount of data (...
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Why my validation loss is not converging over multistream model? [closed]

I want to merge two CNNs that are trained over the different dataset. I have taken two sequential models and merged them. But when using customized fit_generator, validation loss is not converging. ...
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Data Augmentation in Keras: How many training observations do I end up with?

I'm reading through Francois Chollet's "Deep Learning with Python" and was recently introduced to a concept I had never encountered before in my statistics studies. Namely, data augmentation. I have a ...