Questions tagged [convolutional-neural-networks]

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

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24 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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670 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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37 views

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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1answer
47 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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18 views

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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2answers
653 views

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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48 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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2answers
76 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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13 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
108 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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2answers
354 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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62 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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2answers
73 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
38 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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32 views

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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2answers
762 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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83 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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2answers
87 views

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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1answer
186 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
30 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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2answers
83 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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2answers
121 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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0answers
131 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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2answers
93 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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2answers
2k 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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1answer
63 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
262 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
48 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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2answers
92 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
105 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
27 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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3answers
2k views

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
71 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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36 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
46 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
478 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
104 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
74 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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2answers
138 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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2answers
263 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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1answer
50 views

How to solve the AttributeError: 'Ssd' object has no attribute 'freeze_batchnorm' [closed]

I use a modified training script for modeling images with Tensorflow/Keras/Mobilenet_V2. After a few errors that I could solve I now get the following error: ...
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2answers
94 views

How do randomly initialized neural networks behave?

I am wondering how the output of randomly initialized MLPs and ConvNets behave with respect to their inputs. Can anyone point to some analysis or explanation of this? I am curious about this because ...
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2answers
78 views

Can the same input for a plain neural network be used for a convolutional neural network?

Can the same input for a plain neural network be used for CNNs? Or does the input matrix need to be structured in a different way for CNNs compared to regular NNs?
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0answers
44 views

How a game playing agent could identify potential objects and proximity?

Most implementations I'm seeing for playing games like Atari (usually similar to DeepMind's work using DQN) have 4 graphical frames of input fed into 3 convolutional layers which are then fed into a ...
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1answer
33 views

Can I do oversampling by copying the same image multiple times? Will it effect my neural network accuracy?

I am working on an image data-set. As you may have guessed it is imbalanced data. I have 'Class A, 19,000 images' and 'Class B, 2,876 images'. So I did an undersampling by removing randomly from the ...
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32 views

How to preprocess a modified dataset so that a fitted CNN makes correct predictions on an un-modified version of the dataset?

for a school project I have been given a dataset containing images of plants and weeds. The goal is to detect when there is a weed in the pictures. The training and validation sets have already been ...
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1answer
175 views

Most efficient neural network for human activity recognition

A paper from machinelearningmastery.com on human activity recognition states that 1D convolutional neural networks work the best on classification of human activities using data from accelometer. But, ...
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1answer
47 views

Are commercially available neural ICs digital?

Apparently, one can buy a special-purpose integrated circuit (an IC like this one, for instance) to host a convolutional neural network. QUESTION Is such a circuit digital? Except for digital random-...
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1answer
21 views

Image prediction model when data-set classes have visual similarity

Lets say we have a data-set of all cats and we have to identify the cat breed based on given test image. As, the two different cat breeds have visual similarity can we use existing networks (VGG, ...