Questions tagged [convolutional-neural-network]

A convolutional neural network is a form of neural network with an additional convolutional layer, typically used in image & audio analysis. The convolutional layer is essentially a filtering stage defined by the kernel which is used. For example, a convolutional layer could have a kernel which extracts edges from an image towards the goal of learning which objects are in a scene.

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How Does EAST detector implementation with VGG16 look? How many outputs does it have?

I was reading the Efficient and Accurate Scene Text Detector paper and saw the author reference VGG-16 as a possible stem "feature extractor" network. In the paper they say: In our ...
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Trade-off between number of channels and size of convolutional filters

As far as I understand, the common practice in the modern CNN architectures is to use a smaller convolutional filters, but deeper networks with more channels. One of the reason behind this is that one ...
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Questions about adding metadata to a CNN using keras

I have a convolutional neural network and would like to include some metadata. My metadata is in a multiple csv files that correspond to each class and it contains a bunch of geometric properties (...
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Optimisation of neural networks

Do neural networks get optimized by trial and error, by data scientists, or is there some way of optimizing values through accurate mathematical equations?
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What is a sliding-window convolutional neural network?

In the abstract of "U-Net: Convolutional Networks for Biomedical Image Segmentation", the authors mention a sliding-window convolutional neural network. I've found several other articles ...
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Conv1D layer input and output

Consider the following code for Conv1D layer ...
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How to prove Separable Convolution layer is theoretically identical to traditional Convolution?

I have seen the saying that Separable Convolution layer is theoretically identical to traditional Convolution for so many times, but yet no one has pointed out where the proof is. God, I have google ...
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How are weights represented in a convolution neural network?

I have been trying to develop a convolution neural network following some guides online. However, most guides I have encountered gloss over an important detail, which is how to programmatically ...
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Padding in Convolution Formula

Why is it that the formula for each element in a convolution between an image $I$ and a $k \times k$ sized kernel $K$ is $$ (I*K)_{ij}=\sum_{m=0}^{k-1}\sum_{n=0}^{k-1}I_{(i-m),(j-n)}K_{mn}=\sum_{m=0}^{...
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Understanding the convolution formula

According to several sources this formula, or the center originated version of it, is used to calculate an element of a convolution between an image $I$ and a kernel $K$ of size $k \times k$: $$ (I*K)...
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1D CNN Variational Autoencoder Conv1D Size

I am trying to create a 1D variational autoencoder to take in a 931x1 vector as input, but I have been having trouble with two things: Getting the output size of 931, since maxpooling and upsampling ...
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Number and size of dense layers in a CNN

Most networks I've seen have one or two dense layers before the final softmax layer. Is there any principled way of choosing the number and size of the dense layers? Are two dense layers more ...
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DCGAN: why does my generator has less loss then my discriminator?

I have constructed a DCGAN (deep convolutional generative adversarial network) inspired by this github repository. It is written in a more low level Tensorflow code that I tried transforming into ...
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Use convolutional variational autoencoders for time series prediction

I want to use convolutional variational autoencoders for time series prediction. For example, here is the dimension of my data. ...
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Does a Convolutional Layer in a Neural Network learn the correlation between its input signals via its kernel?

I am interested in the theory behing what a convolutional neural network learns with its convolutional operations. I think it learns (useful) kernels which measure the correlation between its input ...
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Is Maxout the same as max pooling?

I've recently read about maxout in slides of a lecture and in the paper. Is maxout the same as max pooling?
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How does strided deconvolution works?

I am trying to understand how the shape of the image changes after deconvolution ? I am trying to understand the example code of convolutional autoencoder from neon. ...
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Discrepancy in probability calculations in paper 'Multi-digit Number Recognition…'

In the paper, 'Goodfellow, I., et al. Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks. ICLR, 2014', on page 10 there is a table which calculate $\log(P(...
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Stuck on deconvolution in Theano and TensorFlow

I'm captivated by autoencoders and really like the idea of convolution. It seems though that both Theano and TensorFlow only support conv2d to go from an array of 2D-RGB (n 3D arrays) to an array of ...
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Tensorflow oscillating Test and Train Accuracy?

I have implemented a CNN with images as input and 101 classes as output. I have applied mean subtraction and normalization to the input before giving it as input to the network. I have also ...
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What is the depth of an image in Convolutional Neural Network?

I am learning cs231n Convolutional Neural Networks for Visual Recognition. The lecture notes introduce the concepts of width, height, depth. For example, In CIFAR-10, images are only of size ...
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How does deep learning helps in detecting multiple objects in single image?

Let's say there are two cars in an image. How can it detect these cars, given that it can detect single car in an image?
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Document classification using convolutional neural network

I'm trying to use CNN (convolutional neural network) to classify documents. CNN for short text/sentences has been studied in many papers. However, it seems that no papers have used CNN for long text ...
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How to predict on part of image after training on other part of image?

I have images of identity cards (manually taken so not of same size) and I need to extract the text in it. I used tesseract to predict bounding boxes for each letter and am successful to some extent ...
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Applying ConvNets to classify motion/video data

How would someone go about using deep learning to classify sign language gestures? For example, suppose I had video files of many different gestures. For any given gesture, I might have many videos of ...
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border_mode for convolutional layers in keras

Keras has two border_mode for convolution2D, same and valid. Could anyone explain what "same" does or point out some documentation? I could not find any document on the net (except people asking that ...
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Trying to figure out how to set weights for convolutional networks

I am working on CNN, and I have some doubts. Let's assume I only want one feature map, just to make things easier. And let's suppose my image is grayscale, to make things even easier. So, let's say my ...
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Question about bias in Convolutional Networks

I am trying to figure out how many weights and biases are needed for CNN. Say I have a (3, 32, 32)-image and want to apply a (32, 5, 5)-filter. For each feature map I have 5x5 weights, so I should ...
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cifar10 official keras example not giving expected accuracy, using sigmoid seems better than relu

In the official Keras example cifar10 there is the following code to train a CNN using keras10. When I tried it, my neural net would not learn at all, I always get around a 10% acuracy, which is ...
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How are 1x1 convolutions the same as a fully connected layer?

I've recently read Yan LeCuns comment on 1x1 convolutions: In Convolutional Nets, there is no such thing as "fully-connected layers". There are only convolution layers with 1x1 convolution kernels ...
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Convolutional Neural Networks in R

I don't see a package for doing Convolutional Neural Networks in R. Has anyone implemented this kind of algorithm in R?
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LeNet for Convolution network?

I keep seeing LeNet used to referring to a convolution network? I am wondering why LeNet is called LeNet? Is it the abbreviation of anything? Is there a difference between LeNet and convolutional ...
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What are the advantages/disadvantages of using Autoencoders over CNNs for image search?

I've seen both of these techniques be used for image search. One difference I can think of is that autoencoders don't rely on labeled data. I'm not sure, but it seems logical therefore that they can ...
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Is there a known convolutional net architecture to calculate object masks for images?

I would like to train a convnet to do the following: Input is a set of single channel (from black to tones of grey to white) pictures with a given object, let's say cars. Target is, for every picture ...
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How to architect ConvNet to ignore top half of image

I'm building a convoluted neural network to teach a toy car, powered by a Raspberry Pi, how to drive based on incoming streams of frames from a webcam mounted on top of the car. The top half of each ...
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how to propagate error from convolutional layer to previous layer?

I've been trying to implement a simple convolutional neural network. But I've been stuck at this problem for over a week. To be specific, assume there are 3 layers in a convolutional pass, marked as ...
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How many images per class are sufficient for training a CNN

I'm starting a project where the task is to identify sneaker types from images. I'm currently reading into TensorFlow and Torch implementations. My question is: how many images per class are required ...
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Back-propagation through max pooling layers

I have a small sub-question to this question. I understand that when back-propagating through a max pooling layer the gradient is routed back in a way that the neuron in the previous layer which was ...
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Highly accurate convnets appear to have random-looking visualized weights?

I'm building a TensorFlow convoluted neural network that isn't getting the accuracy that I hoped for. So I figured I would visualize the learned weights to see where the network might be stumbling. As ...
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Max-pooling vs. zero padding: Loosing spatial information

When it comes to convolutional neural networks there are normally many papers recommending different strategies. I have heard people say that it is an absolute must to add padding to the images before ...
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Why does “Depth = Semantic representation” in convolutional neural networks?

I was watching some videos online about convolutional networks, and the speaker was discussing the concept of running a filter over an image. He said, and it is also shown in the image below, that "...
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Do all layers have the same computational complexity in a ResNet?

Reading the ResNet paper, paragraph 3.3: The convolutional layers mostly have 3×3 filters and follow two simple design rules: (i) for the same output feature map size, the layers have the same ...
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CNN tagging such that each input could have multiple tags

Thanks in advance for reading my question! I've been using CNNs to classify text using Keras and TF. My data is strings "I read the news" or "I read machine learning news" and my ...
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Unable to figure out nInputPlane in SpatialConvolution in torch?

Documentaion for Spatial Convolution define it as module = nn.SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, [dW], [dH], [padW], [padH]) nInputPlane: The ...
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Training Validation Testing set split for facial expression dataset

I am using Convolutional Neural Networks (CNN) and I just want to ask if the way I split my training/validation/testing set is correct. I have a total of 55 subjects. I plan to split them into 80–10–...
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How can I create a classifier using the feature map of a CNN?

I intend to make a classifier using the feature map obtained from a CNN. Can someone suggest how I can do this? Would it work if I first train the CNN using +ve and -ve samples (and hence obtain the ...
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Overfitting after first epoch

I am using convolutional neural networks (via Keras) as my model for facial expression recognition (55 subjects). My data set is quite hard and around 450k with 7 classes. I have balanced my training ...
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Is there any public implementation / publication with Hintons capsules idea?

In Hintons talk "What's wrong about convolutional nets" (Late 2016 or early 2015, I guess) he talks about capsules to make a modular CNN. Is there any publicly available implementation or papers ...
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Applying convolutional neural network over text documents using 1-D tf-idf feature vectors

I want to apply a CNN over documents. I have tf-idf vectors of documents with me (one vector per document). My question is, is 1D CNN applicable in this case? The reason I am asking this question is ...

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