Answers.Body: <p>Yes, Convolutional Neural Networks(CNNs) can and have been applied to non-image problems. Arguably, any problem in which the location of a feature(s) is relevant can be attempted via CNNs. CNNs works under the assumption that points close to each other in the data share some correlations/relationship whist points further apart don't share as much information. So, theoretically, if you can phrase your problem so that it meets this requirement(s), it can be attempted by a convolutional neural network. Here are few applications of CNNss that are dont involve images: </p>
  
  <ul>
  <li><a href="http://ieeexplore.ieee.org/document/6857341/?reload=true" rel="noreferrer">Speech recognition</a></li>
  <li><a href="https://arxiv.org/abs/1509.01626" rel="noreferrer">Character-level Convolutional Networks for Text Classification</a></li>
  <li><a href="https://chara.cs.illinois.edu/sites/sp16-cs591txt/files/0226-presentation.pdf" rel="noreferrer">Text classification using CNNs</a></li>
  <li><a href="http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/" rel="noreferrer">Natural Language Processing</a></li>
  <li><a href="https://arxiv.org/pdf/1703.04691.pdf" rel="noreferrer">Time Series Forecasting</a></li>
  <li><a href="https://arxiv.org/abs/1404.2188" rel="noreferrer">Sentence modeling</a></li>
  <li><p>I've used 1-D CNNs on spectral data. Here are some examples of <a href="https://arxiv.org/abs/1710.01927" rel="noreferrer">CNNs applied to spectral data</a> : </p>
  
  <ul>
  <li><a href="https://pdfs.semanticscholar.org/2af4/f72c2326c15616ad9f5cf18d5278d6608a14.pdf" rel="noreferrer">Spectral Convolutional Neural Network for music classification</a></li>
  <li><a href="http://ieeexplore.ieee.org/document/7730323/" rel="noreferrer">Convolutional neural network based classification for hyperspectral data</a></li>
  </ul></li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2017-11-06T11:11:04.660Z
Answers.LastActivityDate: 2018-07-22T02:15:29.647Z
Answers.LastEditDate: 2018-07-22T02:15:29.647Z
Answers.UserDisplayName: Tshilidzi Mudau
Answers.Body: <p>CNNs are generally designed for 2D (generally image) data. Hence, other usages are most likely a "hack" to CNN logic. </p>
  
  <p>You can check how to extract features and classify texts with CNN through <a href="http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/" rel="nofollow noreferrer">here</a>.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-11-06T06:10:29.393Z
Answers.LastActivityDate: 2017-11-06T06:10:29.393Z
Answers.UserDisplayName: Deniz Beker
Body: <p>I'm new in this argument, my question is:</p>
  
  <p>Can convolution be applied in other contexts different from image recognition?
  Is there a good source to learn from?</p>
  
Tags: <neural-networks><convolutional-neural-networks>
Title: Can Convolutional Neural Networks be applied in domains other than image recognition?
UserDisplayName: Francesco Rizzi
ThreadText: # Can Convolutional Neural Networks be applied in domains other than image recognition?
  
  (Asked by: username_0 on 2017-11-05)
  
  I'm new in this argument, my question is:
  
  Can convolution be applied in other contexts different from image recognition?
  Is there a good source to learn from?
  
  **Tags:** neural-networks, convolutional-neural-networks
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2017-11-06. Score: 8
  
  Yes, Convolutional Neural Networks(CNNs) can and have been applied to non-image problems. Arguably, any problem in which the location of a feature(s) is relevant can be attempted via CNNs. CNNs works under the assumption that points close to each other in the data share some correlations/relationship whist points further apart don't share as much information. So, theoretically, if you can phrase your problem so that it meets this requirement(s), it can be attempted by a convolutional neural network. Here are few applications of CNNss that are dont involve images:
  
  * [Speech recognition](http://ieeexplore.ieee.org/document/6857341/?reload=true)
  * [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626)
  * [Text classification using CNNs](https://chara.cs.illinois.edu/sites/sp16-cs591txt/files/0226-presentation.pdf)
  * [Natural Language Processing](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/)
  * [Time Series Forecasting](https://arxiv.org/pdf/1703.04691.pdf)
  * [Sentence modeling](https://arxiv.org/abs/1404.2188)
  * I've used 1-D CNNs on spectral data. Here are some examples of [CNNs applied to spectral data](https://arxiv.org/abs/1710.01927) :
  
    + [Spectral Convolutional Neural Network for music classification](https://pdfs.semanticscholar.org/2af4/f72c2326c15616ad9f5cf18d5278d6608a14.pdf)
    + [Convolutional neural network based classification for hyperspectral data](http://ieeexplore.ieee.org/document/7730323/)
  
  
  ---
  
  ### Answer by username_2 on 2017-11-06. Score: 0
  
  CNNs are generally designed for 2D (generally image) data. Hence, other usages are most likely a "hack" to CNN logic.
  
  You can check how to extract features and classify texts with CNN through [here](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/).
  
  
  ---
  
  