Questions tagged [convnet]

For questions regarding "Convolutional Neural Networks" (CNN)

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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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2answers
1k views

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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0answers
1k views

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 ...
1
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1answer
643 views

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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5answers
9k views

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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2answers
1k views

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 ...
10
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2answers
23k views

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 ...
2
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1answer
186 views

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 ...
7
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1answer
14k views

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 ...
2
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1answer
125 views

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 ...
5
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2answers
246 views

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 ...
11
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2answers
6k views

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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4answers
4k views

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?
5
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1answer
3k views

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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2answers
1k views

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 ...
6
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3answers
4k views

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 ...
2
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0answers
78 views

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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2answers
6k views

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. ...
3
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2answers
1k views

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?
9
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2answers
149 views

Are there studies which examine dropout vs other regularizations?

Are there any papers published which show differences of the regularization methods for neural networks, preferably on different domains (or at least different datasets)? I am asking because I ...
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4answers
15k views

How do subsequent convolution layers work?

This question boils down to "how do convolution layers exactly work. Suppose I have an $n \times m$ greyscale image. So the image has one channel. In the first layer, I apply a $3\times 3$ ...
3
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1answer
114 views

Can the size of a pooling layer be learned?

As far as I understood it, the pooling layer doesn't learn anything. It has several parameters, most important its pool_size and ...
196
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10answers
186k views

What are deconvolutional layers?

I recently read Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, Trevor Darrell. I don't understand what "deconvolutional layers" do / how they work. The ...
6
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2answers
3k views

Convolutional neural network for sparse one-hot representation

I have some basic features which I encoded in a one-hot vector. Length of the feature vector equals to 400. It is sparse. I saw that conv nets is applied to a dense feature vectors. Is there any ...
5
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1answer
831 views

Neural Network Golf: smallest network for a certain level of performance

I am interested in any data, publications, etc about what is the smallest neural network that can achieve a certain level of classification performance. By small I mean few parameters, not few ...
41
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2answers
38k views

How to prepare/augment images for neural network?

I would like to use a neural network for image classification. I'll start with pre-trained CaffeNet and train it for my application. How should I prepare the input images? In this case, all the ...