Questions tagged [convolutional-neural-networks]

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

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How is the convolution operation used in CNNs a special case of the convolution operator?

How is the convolution operation used in convolutional neural networks (CNNs) a special case of the mathematical convolution operator? Most of us, when we think of the "convolution operation", we ...
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75 views

Siamese Network for unknown object

I am currently trying to create a One-Shot network using the Siamese architecture for an object that isn't a face. My problem is, in normal Face Recognition the detecting gadget (e.g. Smartphone) ...
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Count number of objects in image using CNN

I'm looking for neural network architecture that excel in counting objects. For example, CNN that can output the number of balls (or any other object) in a given image. I already found articles about ...
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How does the process of segmentation of face in face recognition work?

How does the process of segmentation of face, using a CNN, in face recognition, work? How are we able to segment the face?
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644 views

Why does a fully connected layer only accept a fixed input size?

I'm studying how SPP (Spatial, Pyramid, Pooling) works. SPP was invented to tackle the fix input image size in CNN. According to the original paper https://arxiv.org/pdf/1406.4729.pdf, the authors say:...
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229 views

1D GAN not converging

I am trying to build a 1D GAN able to produce data similar to the input one, which looks like this: I am using pytorch. This is the code for my Discriminator, which takes as input a 1D vector of size ...
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59 views

How to study the correlation between GAN's input vector and output image

A generative adversarial network (GAN) takes a vector of numbers as input and generates an image, based on the input. Each element of the vector causes some feature of the image to change, but the ...
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1answer
17 views

Using convnet to classify language of text contained in images

I hope this question is not too broad or general. I have a very large set of images all of which contain text (some have more, some less). All of them have been tagged as containing, say, English text ...
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Calculating tangent vector of curve s(P,$\alpha$) at given point $\alpha$ = 0

I am reading the paper "Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation", where the tangent vector is calculated for the given curve $s(P,\alpha)$ at $\...
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1answer
32 views

Having trouble understanding some of the details of R-CNN (first one)

Here is what I understand (what I think I understand). We first train out model on our images using transfer learning. So now we have a pre-trained model. For each image in out dataset, we compute ...
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958 views

Pseudocode for CNN with Bounding Box and Classifier

I've been looking at various bounding box algorithms, like the three versions of RCNN, SSD and YOLO, and I have noticed that not even the original papers include pseudocode for their algorithms. I ...
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What is the right way to convolve over word embeddings?

I have two word embeddings $w_1$ and $w_2$ with dimension 100 as input to a convolutional neural network. It should learn the similarity between these two words. I am now concerned with the applied ...
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58 views

Mnist CNN Architecture

In this tutorial from Jeremy Howard: What is torch.nn really? he has an example towards the end where he creates a CNN for mnist. In nn.Conv2d he makes the ...
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Feature visualization on neural networks which are not for classification

Feature visualization allows to better understand neural networks by generating images that maximize the activation of a specific neuron, and therefore understand what are the abstract features that ...
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How to use CNN for making predictions on non-image data?

I have a dataset which I have loaded as a data frame in Python. It consists of 21392 rows (the data instances, each row is one sample) and 1972 columns (the features). The last column i.e. column 1972 ...
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50 views

How to train CNN such it eliminate dependent features and focuses on independent ones?

How we should train a CNN model when training dataset contains only limited number of cases, and the trained model is supposed to predict class (label) for several other cases, which has not seen ...
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88 views

Which neuron represents which part of the input?

In a neural network, each neuron represents some part of the input. For example, in the case of a MNIST digit, consider the stem of the number 9. Each neuron in the NN represents some part of this ...
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14 views

Sample from a distribution inside a NN layer

Is it possible to sample from a distribution inside a neural network forward function? Assume that there is a NN and a sample is needed to be derived from it at every forward-pass to randomly set a ...
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1answer
145 views

Reduce receptive field size of CNN while keeping its capacity?

I have a convolutional encoder (a CNN) consisting of DenseBlocks and a total of 50 layers (cf. FC-DenseNet103). The receptive field of the encoder (after last layer) is 660 according to Tensorflow ...
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744 views

Using GAN's to generate dataset for CNN training

I'm doing bachaleor thesis on traffic sign detection using single shot detector called YOLO. These single shot detectors can perform detection of objects in image and so they have specific way of ...
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101 views

How can VAE have near perfect reconstruction but still output junk when using random noise input

I am creating a VAE for time series data using CNNs. The data has 4800 timesteps and 4 features. It is standardized and normalized. The network I am using is implemented in Keras as follows. I have ...
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85 views

Choosing the right neural network settings

I'm trying to train a neural network on evaluating chess positions if rather white (0.0) or black would win (1.0) Currently the input consists of 4 bits per chess field (piece id 0 - 12, equals 64*4)....
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1answer
59 views

Testing, Validation Percentage & Test, Validation Batch Size Difference?

I'm doing transfer learning using Inception on Tensorflow. The code that I used for training is https://raw.githubusercontent.com/tensorflow/hub/master/examples/image_retraining/retrain.py If you ...
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How does a neural network output text box location data?

I'm interested in creating a convolutional neural network or LSTM to locate text in an image. I don't want to OCR the text yet, just find the text regions. Yes, I know Tesseract and other systems can ...
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27 views

Using batches in testing

If one examines SSD: Single Shot MultiBox Detector code from GitHub repository, it can be seen that, for a testing phase (evaluating network on test data set), there is a parameter ...
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1k views

What's the role of bounding boxes in object detection?

I'm quite new to the field of computer vision and was wondering what are the purposes of having the boundary boxes in object detection. Obviously, it shows where the detected object is, and using a ...
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117 views

Tensorflow : Inception V3 Transfer Learning Parameter Tuning

Sorry if my question is at the wrong place, I'm new in this community. So, I have dataset with total of 1 million images (augmented) that separated in 28 classes. I followed this tutorial https://www....
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Keras simple CNN not learning [closed]

I was trying to write a simple CNN in keras during a course, and I wrote one that does not learn at all, but I don't understand why. Don't bother about the coding, first I load two images of a dog ...
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294 views

Additive Attention in Convolutional Networks

Attention has been used widely in recurrent networks to weight feature representations learned by the model. This is not a trivial task since recurrent networks have a hidden state that captures ...
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1answer
32 views

Pixel-Level Detection of Each Object of the Same Class In an Image

I have source data that can be represented as a 2D image of many similar curves. They may oftentimes cross over one another, so regions of interest will overlap. My goal is to implement a neural ...
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91 views

CNN-feature extraction

Is there any way to control the extraction of features?How to recognize what features are been learnt during training i.e relevant information is been learnt or not?
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137 views

Can machine learning algorithms (CNNs?) be used/trained to differentiate between small differences in details between images?

I was wondering if machine learning algorithms (CNNs?) can be used/trained to differentiate between small differences in details between images (such as slight differences in shades of red or other ...
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167 views

How does DARTS compare to ENAS?

How does DARTS compare to ENAS? Which one is better or what advantages does they each have? Links: DARTS: Differentiable Architecture Search Efficient Neural Architecture Search via Parameter ...
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106 views

How data augmentation like rotation affects the quality of detection?

I'm using an object detection neural network and I employ data augmentation to increase a little my small dataset. More specifically I do rotation, translation, mirroring and rescaling. I notice that ...
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4k views

Learning Rotated bounding box for object detection

I have checked out many methods and paper like yolo, ssd, etc with very promising result in detecting a rectangular box around object, But could not find any paper, which shows an learning a rotated ...
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66 views

Measuring Width of Crack

Are there any projects where you can detect and measure the width of a crack? I am using tensorflow and labeling the data sets for now.
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1answer
392 views

Dice loss gives binary output whereas binary crossentropy produces probability output map

On recommendation of Kanak on stackoverflow I am posting this question here: Currently I am experimenting with various loss functions and optimizers for my binary image segmentation problem. The loss ...
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3answers
89 views

Dimension after multiple convolutions in ConvNets

I'm trying to understand exactly what does a convnet do to what, and I have trouble finding the dimensions alongside the convolutions. If we take VGG 16 architecture, how do I get from 224x224x3 to ...
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142 views

Why do we throw out negative ReLU value?

when using Rectified Linear Unit after convolution layers we have to have twice as much filters to be able to detect features (eg both left and right edge detector). Why do we just throw out negative ...
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1answer
123 views

Understanding the application of Sobel kernel followed by ReLU to a zero-padded image

Let's say I have a $2 \times 2$ pixel of grayscale picture, where there is one edge such that the left pixel contains a value, 30, and the right pixels contain a value 0 (in red below). And for edge ...
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1answer
32 views

Smaller interest area for images than the size of the image in classification neural networks

I have the following binary classification problem, my labeled dataset contains images 96x96 px. Now in every image the interest area is of size 32x32 px in the center of the image, and the images are ...
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What is the concept of channels in CNNs?

I am trying to understand what channels mean in convolutional neural networks. When working with grayscale and colored images, I understand that the number of channels is set to 1 and 3 (in the first ...
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1answer
82 views

Appropriateness of 3D Convolutional Neural Network for segmentation of medical image data

I have a couple different segmentation tasks that I would like to perform on medical imaging data using CNN's. I'm currently trying to wrap my head around how well a 3D network might work, using a U-...
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50 views

Image Segmentation Prediction with cropping 256x256 grids is very slow

I have only a limited dataset (<25) with large-sized images (>1500x2000) and their pixelwise labels. The aim is to find unusual patterns in this industry dataset and highlight them. To generate ...
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1answer
49 views

Relationship between input range and channel means, standard deviations for CNNs

So, I'm using a pretrained pnasnet5large model to do some image classification (https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py) In the file, it ...
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2answers
803 views

Is pooling a kind of DropOut

If I got well the global idea of DropOut it allows to improve the sparsity of the information that come from one layer to another by setting some weights to zero. In another hand, pooling, let's say ...
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2answers
112 views

How to approach this handwritten digit recognition?

I have multiple pictures that look exactly like the one below this text. I'm trying to train CNN to read the digits for me. Problem is isolating the digits. They ...
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1answer
78 views

Recognition of small objects

I'm currently implementing an Android app for street sign recognition. My solution works quite well for the GTSRB dataset, since it provides a labeled test set of centered images. However, it doesn't ...
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298 views

How to get a binary output from a Siamese Neural Network

I'm trying to train a Siamese network to check if two images are similar. My implementation is based on this. I find the Euclidian distance of the feature vectors(the final flattened layer of my CNN) ...
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364 views

Keras giving memory allocation error and running extremely slow

I am working on character recognition using convolutional neural networks. I have 9 layer model and 19990 training data and 4470 test data. But when I am using keras with Tensorflow backend. When I ...

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