Answers.Body: <p>This problem is known as "style transfer". 
  The field was started by <a href="https://arxiv.org/pdf/1508.06576.pdf" rel="nofollow noreferrer">Gatsys et al. in 2016</a>, and has seen a lot of work over the last few years, including conditional translation from <a href="https://arxiv.org/abs/1611.07004" rel="nofollow noreferrer">Isola et al. in 2017</a> and unpaired translation from <a href="https://arxiv.org/pdf/1703.10593.pdf" rel="nofollow noreferrer">Zhu et al. 2017</a>.</p>
  
  <p>This picture (Figure 2 from Gatsys et al.) shows the idea behind style transfer, and illustrates the dramatic results that are possible with these models.</p>
  
  <p><a href="https://i.sstatic.net/jNCf2.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/jNCf2.jpg" alt="enter image description here"></a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-12T19:50:28.900Z
Answers.LastActivityDate: 2020-04-12T20:00:39.897Z
Answers.LastEditDate: 2020-04-12T20:00:39.897Z
Answers.UserDisplayName: Chris Cundy
Body: <p>Simplified: <em>What is it called in AI when a program is designed to make "x in the style of y;" when it trains off of two types of sources in order to make a thing from source one, informed by features from source two?</em> For example, if a network made up of two smaller networks were to take sheet music of a specific compositional style in network A and audio samples from a certain genre in B and through an interface creates music from a certain genre in a certain compositional style; the <em>is</em> comes from One, the <em>seems</em> comes from Two.</p>
  
  <p>For more coarse and obvious examples:</p>
  
  <ul>
  <li>"Compose synthpop in the style of Beethoven"</li>
  <li>"Draw impressionism in the style of Mondrian"</li>
  <li>"Generate casserole recipes using only ingredients most likely to fluctuate in price given current market data"</li>
  <li>"Sketch baseballs that look like they're made of espresso foam"</li>
  </ul>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-12T03:29:16.813Z
Comments.Text: [Style Transfer](https://www.tensorflow.org/tutorials/generative/style_transfer)
Comments.UserDisplayName: Chris Cundy
Tags: <neural-networks><machine-learning><terminology>
Title: What is it called in AI when a program is designed to make "x in the style of y"?
UserDisplayName: JohnnyApplesauce
ThreadText: # What is it called in AI when a program is designed to make "x in the style of y"?
  
  (Asked by: username_0 on 2020-04-12)
  
  Simplified: *What is it called in AI when a program is designed to make "x in the style of y;" when it trains off of two types of sources in order to make a thing from source one, informed by features from source two?* For example, if a network made up of two smaller networks were to take sheet music of a specific compositional style in network A and audio samples from a certain genre in B and through an interface creates music from a certain genre in a certain compositional style; the *is* comes from One, the *seems* comes from Two.
  
  For more coarse and obvious examples:
  
  * "Compose synthpop in the style of Beethoven"
  * "Draw impressionism in the style of Mondrian"
  * "Generate casserole recipes using only ingredients most likely to fluctuate in price given current market data"
  * "Sketch baseballs that look like they're made of espresso foam"
  
  **Tags:** neural-networks, machine-learning, terminology
  > **Comments:**
  > **username_1**: [Style Transfer](https://www.tensorflow.org/tutorials/generative/style_transfer)  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-04-12. Score: 2 | Accepted answer
  
  This problem is known as "style transfer".
  The field was started by [Gatsys et al. in 2016](https://arxiv.org/pdf/1508.06576.pdf), and has seen a lot of work over the last few years, including conditional translation from [Isola et al. in 2017](https://arxiv.org/abs/1611.07004) and unpaired translation from [Zhu et al. 2017](https://arxiv.org/pdf/1703.10593.pdf).
  
  This picture (Figure 2 from Gatsys et al.) shows the idea behind style transfer, and illustrates the dramatic results that are possible with these models.
  
  [![enter image description here](https://i.sstatic.net/jNCf2.jpg)](https://i.sstatic.net/jNCf2.jpg)
  
  
  ---
  
  