Questions tagged [convolutional-neural-networks]

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

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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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Why everyone is using CNN for image segmentation?

I'm a newbie in artificial intelligence. I have started to research how to do image segmentation and all the papers that I have found are about CNN. Most of them use the same network, U-net, but with ...
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How many ways are there to perform image segmentation?

I'm new in Artificial Intelligence and I want to do image segmentation. Searching I have found these ways Digital image processing (I have read it in this book: Digital Image Processing, 4th edition)...
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1answer
44 views

When training a CNN, what are the hyperparameters to tune first?

I am training a convolutional neural network for object detection. Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I read ...
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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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42 views

Set my own kernels to a CNN and don't let it to modify it

I'm newbie in Convolutional Neural Networks and I have discovered (and I hope I'm right) that kernels in convolutional layers are learned while training. If I have a kernel that it is very good to ...
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What are some ways to quickly evaluate the potential of a given NN architecture?

Main question Is there some way we can leverage general knowledge of how certain hyperparameters affect performance, to very rapidly get some sort of estimate for how good a given architecture could ...
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Is it normal to see oscillations in tested hyperparameters during bayesian optimisation?

I've been trying out bayesian hyperparameter optimisation (with TPE) on a simple CNN applied to the MNIST handwritten digit dataset. I noticed that over iterations of the optimisation loop, the tested ...
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89 views

Suggestion for finding the stable regions in spiral galaxy data?

I am working with a data set that consists of the actual pitch angle (given as PA(Y)) and the pitch angle at each radii (listed from 1 to 217). In the image below, ...
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1answer
48 views

Semantic Segmentation For Multiple Objects When Trained On Single Object

More of a conceptual question here: I'm working on semantic segmentation tasks in the medical space using the U-Net. Let's say that I train a U-Net model on medical images with the goal of segmenting ...
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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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1answer
360 views

What is a cascaded convolutional neural network?

For a project I am doing, I found the paper Face Alignment in Full Pose Range: A 3D Total Solution. It is using a cascaded convolutional neural network, but I wasn't able to find the original paper ...
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How can I do hyperparameter optimization for a CNN-LSTM neural network?

I have built a CNN-LSTM neural network with 2 inputs and 2 outputs in Keras. I trained the network with model.fit_generator() (and not ...
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Autoencoder produces repeated artifacts after convergence

As experiment, I have tried using an autoencoder to encode height data from the alps, however the decoded image is very pixellated after training for several hours as show in the image below. This ...
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1answer
91 views

How to formalize learning in terms of information theory?

Consider the following game on a MNIST dataset: There are 60000 images. You can pick any 1000 images and train your Neural Network without access to the rest of images. Your final result is ...
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1answer
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How are small scale features represented in an Inverse Graphics Network (autoencoder)?

This post refers to Fig. 1 of a paper by Microsoft on their Deep Convolutional Inverse Graphics Network: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/kwkt_nips2015.pdf Having ...
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2answers
280 views

Calculation of FPS on object detection task

How to calculate mean speed in FPS for an object detection model like YOLOv3 or YOLOv3-Tiny? Different object detection models are often presented on charts like this: I am using the DarkNet ...
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2answers
91 views

What is the reasoning behind the number of filters in the convolution layer?

Let's assume an extreme case in which the kernel of the convolution layer takes only values 0 or 1. To capture all possible patterns in input of $C$ number of channels, we need $2^{C*K_H*K_W}$ filters,...
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Which CNN hyper-parameters are most sensitive to centered versus off centered data?

Which hyper-parameters of a convolutional neural network are likely to be the most sensitive to depending on whether the training (and test and inference) data involves only accurately centered images ...
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1answer
61 views

How to create a fully connected(matrix) layer with vector input

I am trying to replace last fully connected layer of size 4096/2048 with a matrix of size 100x300 with previous fc layer output of 2048. I've tried 2D convolution - to map from 2048 --> 100x300 (...
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How to understand my CNN's training results?

I created a multi-label classification CNN to classify chest X-ray images into zero or more possible lung diseases. I've been doing some configuration tests on it and analyzing its results and I'm ...
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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
47 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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29 views

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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1answer
38 views

Accuracy scores in a Deep Learning project

I'm using three pre-trained deep learning models to detect vehicles and count from an image data set. The vehicles belong to one of these classes ['car', 'truck', 'motorcycle', 'bus']. So, for a ...
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1answer
192 views

How many parameters are being optimised over in a simple CNN?

Okay so here's my CNN (simple example from a tutorial) along with some arithmetic to get the total number of free parameters. We've got a dataset of 28*28 grayscale image (MNIST). First layer is a ...
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1answer
34 views

How to explain peak in training history of a convolutional neural network?

I am training a simple convolutional neural network to recognize two types of 1024-point frequency spectra (FFT). This is the model I'm using: ...
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19 views

Flattened vector observation or convolutional neural network input?

This is more of a general question of how to model/preprocess 'visual' state-observations to an Agent in Reinforcement Learning that I'll illustrate with an example. Say you have a reinforcement ...
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2answers
55 views

How can I convert the probability score between 0 to 1 to another format?

I have trained a multi-class CNN model using fastai. The model splits out probabilites for each of the three classes, which, of course, sum up to 1. The class with highest probability becomes the ...
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176 views

What is a front-end and back-end in the context of convolutional neural networks?

The title is mainly it really. I googled that phrase or variations of it and couldn't find a decent result.
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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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25 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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24 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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1answer
36 views

Train a competitive layer on nonnormalized vectors using LVQ technique

How can we train a competitive layer on non-normalized vectors using LVQ technique ? an example is given below from Neural Network Design (2nd Edition) book The net input expression for LVQ networks ...
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2answers
109 views

How can we compute the gradient of max pooling with overlapping regions?

Studying CNN Back-propagation I can't understand how can we compute the gradient of max pooling with overlapping regions ? That's also a question from this quiz and can be also found on this book .
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How to draw bounding boxes for gender classification?

I wonder what is the better way of drawing rectangles on images for gender classification. My task is to create a classifier (CNN based) to detect gender from pictures of entire bodies (not just faces)...
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1answer
541 views

How to compute number of weights of CNN?

How can we compute number of weights considering a convolutional neural network that is used to classify images into two classes : INPUT: 100x100 gray-scale images. LAYER 1: Convolutional layer with ...
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20 views

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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1answer
26 views

Most suitable model for video classification with a fixed camera

Consider a fixed camera that records a given area. Three things can happen in this area: No action People performing action A People performing action B I want to train a model to detect when ...
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2answers
71 views

What makes GAN or VAE better at image generation than NN that directly maps inputs to images

Say a simple neural network's input is a collection of tags (encoded in some way), and the output is an image that corresponds to those tags. Say this network consists of some dense layers and some ...
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25 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
61 views

Why do current models use multiple normalization layers?

In most current models, the normalization layer is applied after each convolution layer. Many models use the block $\text{convolution} \rightarrow \text{batch normalization} \rightarrow \text{ReLU}$ ...
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79 views

Which neural network should I use to transform the pixels of a video overtime?

I want to train a network with video data and have it transform pixel values overtime on an input video. This is for an art project and does not need to be super elaborate, but the videos I want to ...
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1answer
28 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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374 views

Convolutional Neural Network: does each filter in each convolution layer create a new image?

Say I have a CNN with this structure: input = 1 image (say, 30x30 RGB pixels) first convolution layer = 10 5x5 convolution filters second convolution layer = 5 3x3 convolution filters one dense layer ...
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1answer
48 views

Can a trained object detection model deal with variations of the input?

Suppose an object detection algorithm is good at detecting objects and people when an object and person is close to a camera and upright. If the person walks farther away from the camera and is "...
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34 views

FasterRCNN's RPN network training

I would like to know if my understanding of RPN training is correct, and if never training the RPN on some specific anchor box is bad (i.e if the anchor never sees good nor bad examples). To make my ...
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18 views

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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57 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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43 views

structure of neural network for classification problems with large amounts of null classifications

I am building a Convolution neural network to predict certain categories based on images (the location of a pointer on a surface) . However in many cases there will be no pointer in the view or ...

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