Questions tagged [convolutional-neural-networks]

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

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50 views

How is the receptive field of a CNN affected by transposed convolution?

When computing receptive field recursively through a CNN, does a transposed convolution affect the receptive field the same way that a convolution does if the kernel and stride is the same?
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Standardize images using ImageDataGenerator in keras

I was trying to normalize my input data images for feeding to my convolutional neural network and wanted to use standardize my input data. I referred to this article: https://stackoverflow.com/...
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Weird border artifacts when training a CNN

I've been trying to use this DeepLabv3+ implementation with my dataset (~1000 annotated images of the same box, out of the same video sequence): https://github.com/srihari-humbarwadi/...
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Specifying resolution for objects with known dimensions using CNN

I would like to ask you for advice. I deal with beekeeping but I am also a bit a programmer and an electronics specialist. And this is where my 3 interests come together, actually 4 because recently ...
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Can neural style transfer work on the image style in this question or is there a better technique?

I've been working with this neural style paper https://arxiv.org/pdf/1508.06576v2.pdf to try and transfer the style from this image to photos of pets. In case you're not familiar with the technique, I'...
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What is the use of concatenate layer in CNN?

I am not asking what does concatenate layer does in general in point of mathematical operation. But at feature level, what significance does it provide. Does it helps removing false negatives or does ...
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Why would my neural network run faster on my laptop than on my university's supercomputer?

I am trying to get my neural network running on my university's supercomputer in order to decrease its runtime (not for training, for testing - feedforward runs only). However, the Matlab function I ...
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1answer
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Hand-Signs Recognition using Deep Learning Convolutional Neural Networks

I am developing a CNN model to recognize 24 hand-signs of American Sign Language. I have 2500 Images/hand-sign. The data split is: Training = 1250 Images/hand-sign Validation = 625 Images/hand-sign ...
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Positive bias causes the calculation of incorrect gradients

I have a data set with a positive bias (an image, where the values range from 0 to 1), that seems to be causing my network to calculate incorrect gradients. If I just use the raw image as input, of ...
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Acoustic Input Data: Decibel or Pascals

In acoustics decibel levels were defined to solve an issue with showing values that are interpretive, understandable, and easy to communicate in contrast to intensity or pressure in Pascals. $dB = ...
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Transpose convolution in TiF-GAN: How does “same” padding works?

This question should be quite generic but I faced the problem in the case of the TiF-GAN generator so I am going to use it as an example. (Link to paper) If you check the penultimate page in the ...
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Text detection on English and Chinese language

https://arxiv.org/abs/1910.07954 In this paper, we have a convolutional character neural network where we have object detection by taking a character as a basic unit. First, we do character detection ...
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How can we combine different deep learning models?

I know that ensembles can be made by combining sklearn models with a VotingClassifier, but is it possible to combine different deep learning models? Will I have to make something similar to Voting ...
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What is generally the best way to combine tabular image metadata with image data in a convolutional neural network?

I have 26 features from tabular data (clinical variables from patients like age gender etc) that I want to add to my cnn which is using xray images from patients. I am using the inception network. ...
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25 views

Scoring feature vector with Support Vector Machine

I am reading the R-CNN paper by Ross Girshick1 et al. (link) and I fail to understand how they do the inference. This is described in the section 2.2.Test-time Detection in the paper. I quote: At ...
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41 views

Reinforcement learning CNN input weakness

I'm trying to train a network to navigate a 48x48 2D grid, and switch pixels from on to off or off to on. The agent receives a small reward if correct, and small punishment if incorrect pixel plotted. ...
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Training dataset for convolutional neural network classification - will images captured on the ground be useful for training aerial imagery?

I am an agronomy graduate student looking to classify crops from weeds using convolutional neural networks (CNNs). The basic idea that I am wanting to get into involves separating crops from weeds ...
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YOLOv3 Synthetic Data Training

Suppose we want to train a model to detect various objects. Let's say we have training data of those objects in various backgrounds along with their bounding boxes. Basically these objects have been ...
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24 views

Is it possible to combine multiple SVMs that were trained on sublayers of a CNN into one combined SVM?

I have created a CNN for use on the MNIST dataset for now (so I have 10 classes). I have trained SVMs on the sublayers of this trained CNN and wish to combine them into a combined SVM as to give a ...
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1answer
54 views

not sure if fine-tuned network is finely-tuned

I am practicing with Resnet50 fine tuning for binary classification task, here is my code snippet. ...
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Is the number of neurons in each capsule in a capsule neural network hardcoded?

The capsule neural networks have been formally introduced in the paper Dynamic Routing Between Capsules. Much ado has been made about how the capsules output a vector (magnitude = probability that an ...
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Optimizer effects on neural network with two outputs

I'm confused about the following issue. Let assume that we have a neural network that takes one input and two outputs. I try to visualize my model like as follows: ...
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29 views

Imposing contraints on sequence of image classifications

Are there example implementations of networks that apply constraints across sequences of image classifications where class labels are ordinal numbers? For example, to cause the output of a CNN to ...
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35 views

Affine Transformations and Data Augmentation

If you have a very distorted video/image, would affine transformations of the images make object detection algorithms make more mistakes compared to a normal camera?
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How to train image segmentation task with only one class?

Is there a neural network that has architecture optimizations for segmenting only one class (object and background)? I have tried U-net but it is not providing good enough results. I am wondering if ...
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37 views

Using U-NET for image semantic segmentation

I'm getting literally crazy trying to understand how U-NET works. Maybe it is very easy, but I'm stuck (and I have a terrible headache). So, I need your help. I'm going to segment MRI to find white ...
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1answer
34 views

Is CNN capable of extracting the descriptive statistics features

I was trying to build a CNN model. I used time series data of daily temperature to predict if there is risk of an event, say bacteria growth. I calculated the descriptive statistics of the time series,...
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Optimisation of dependence of efficiency of CNN on training data

I got a large dataset of images (dimensions of 16 x 16, 250k samples) and corresponding spherical coordinates (distributed uniformly in each coordinate). On these, I trained a convolutional regression ...
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87 views

Is there an audio dataset with the corresponding phonemes in the audio?

I am looking for a dataset of clear audio, a corresponding transcript (optional), and most importantly a list of all the phonemes said in the audio, with the length of each phoneme and a mention of ...
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132 views

In Faster R-CNN, how can I get the predicted bounding box given the neural network's output?

The RPN loss in Faster RCNN paper is $$ L({p_i}, {t_i}) = \frac{1}{N_{cls}} \sum_{i} L_{cls}(p_i,p_i^*) + \lambda \frac{1}{N_{reg}} \sum_i p_i^* L_{reg}(t_i, t_i^*) $$ For regression problems, we have ...
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34 views

Is weight pruning applied to all layers or only to dense layers in CNNs?

I was reading about weight pruning in convolutional neural networks. Is it applied for all the layers including convolutional layers or only it is done for dense layers?
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Can Grad CAM feature maps be used for Training?

I am trying to recreate the architecture of the following paper: https://arxiv.org/pdf/1807.03058.pdf Can someone help me in explaining how are the feature maps coming out of the output of GradCam ...
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21 views

Keras correlation coefficient as network metric in R

does anyone know how to use the correlation coefficient or squared correlation coefficient as a metric in keras in R (although other languages may provide clues). This is for a CNN that functions ...
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1answer
50 views

How can max-pooling be applied to find features in words?

I'm reading about max-pooling in a dynamic CNN paper. I can see how it can help find features in images, given that the pixel with the highest density gets pooled, but how does it help to find ...
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How to count pixels in a object mask which is segmented using Mask R-CNN?

I have segmented concrete cracks from concrete structure images using Mask R-CNN. Now I need to measure the length of the segmented masked crack. Will the pixel counting method work? Can anyone help?...
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How to measure object size from the disparity map using CNN?

I am a student learning about image processing using CNN. I want to learn how to measure the object size from the disparity map obtained from left and right stereo images.
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How to measure the size of an crack which is segmented from an image using Mask-RCNN?

I am a masters student going to work in a project to analyze the cracks in underwater concrete structures. I need some suggestions for data acquisition and length measurement of the crack. I have ...
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30 views

How to implement CNN with variable number of images in tensorflow or keras?

Suppose I have a problem where I want to classify the color of LEDs seen in the image. I can use OpenCV to pinpoint the exact location of these LEDs but I do not know their color for sure because the ...
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19 views

CNN multi output scores and evaluation

I am building a CNN with two outputs. I still have to put time in the network itself, but I was trying to get a good evaluation/classification report of the results. My code is the following: ...
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Would models like U-Net be able to segment objects which has label based on its surrounding context?

Suppose that we want to segment a red blob from the image, normally you will have a class for this red blob e.g. 0. And every red blob you detected will have a class of 0. But, in my case, I want ...
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Is there any time-varying directed graph dataset?

I am interested in the node classification task for graph data. So far,I've tried it with the Cora dataset, but it is an undirected graph and has word attributes as features. I want to extend this ...
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How to get same accuracy with identical models in Keras and Tensorflow?

As we all know Keras backend uses Tensorflow and so it should give out same kind of results when we provide same parameters, hyper-parameters, weights and biases initialisation at each layer, but ...
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191 views

Threshold selection for Siamese network hyper-parameter tuning

I'm interested in modeling a Siamese network for facial verification. I've already written a simple working model that inputs feature vectors generated from two CNNs with shared weights then outputs a ...
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1answer
200 views

Matterport MRCNN and multiclass classification

I want to create a model which solve a multiclass classification problem. The main concept is: every picture contain only one object the background is very simple all object is coming from the same ...
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19 views

Sliding Window Detection

Suppose that we have a labeled training set of $n$ closely cropped images of cars $(x_1, y_1) , \dots, (x_n, y_n)$. We then train a CNN on this. Let's say we have $m$ test images. Then for each of the ...
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382 views

Which approaches are best suited for text deblurring?

I want to deblur text images using deep learning. Which approaches are best suited for the task? Any example networks? Is unsupervised network the best approach? GAN or cycle GAN for these purposes? ...
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40 views

Reference request: one-hot encoding outperforming random orthogonal encoding

I experimented with a CNN operating on texts encoded as sequences of character vectors, where characters are encoded as one-hot vectors in one embedding and as random unit length pairwise orthogonal ...
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CNN - Visualizing images near decision boundary - Pixels inexplicably tend to edges

We are exploring the images classified by a CNN at its decision boundary, using Genetic Algorithms to generate them. We have created a fine-tuned binary grayscale image classifier for cats. As the ...
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How to train and update weights of filters

I have some problems with training CNN :( For example: Input 6x6x3, 1 core 3x3x3, output = 4x4x1 => pool: 2x2x1 By backpropagation I calculated deltas for output. This tutor and other tutors are ...
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Super Resolution CNN generates black dots on output images

I have been trying to train a CNN for the super-resolution task based on the work of Dong et al., 2015 [1]. The network structure built in PyTorch is as follows: ...

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