Answers.Body: <p>Your question is a bit broad. Some examples of creativity and how it's achieved (in all of them there is an ordinary convolutional network trained via supervised learning, but applied differently):</p>
  
  <ul>
  <li><p>Google Deep Dream uses <a href="https://cs.stackexchange.com/questions/56406/google-deepdream-elaborated/82074">backpropagation to the image</a> to tweak the image towards maximum activation in the convolutional layer. As a result, it finds lots of "things" in the image and enhances them:</p>
  
  <p><a href="https://i.sstatic.net/0Hrgx.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/0Hrgx.jpg" alt="deep-dream"></a></p></li>
  <li><p><a href="http://kvfrans.com/generative-adversial-networks-explained/" rel="nofollow noreferrer">Generative Adversarial Networks</a> use two competing networks (generator and discriminator) to generate samples from the training manifold:</p>
  
  <p><a href="https://i.sstatic.net/tDGXt.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/tDGXt.jpg" alt="generated-bedrooms"></a></p></li>
  <li><p><a href="https://github.com/jcjohnson/neural-style" rel="nofollow noreferrer">Neuro style</a> method captures the style of one image via Gram matrix of convolutional layer activations and trains (another) neural network with a special loss function that minimizes the joint distance to the incoming image (content) and to the Gram matrix (style). The result:</p>
  
  <p><a href="https://i.sstatic.net/8LbQk.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/8LbQk.jpg" alt="neuro-style"></a></p></li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-10-11T13:04:58.773Z
Answers.Text: Thanks for the reply.. And for giving this overall reply.. It surely helps with the understanding, or what too google for too for my further investigations.
Answers.UserDisplayName: Carlton Banks
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-10-11T11:38:46.880Z
Answers.LastActivityDate: 2017-10-11T11:38:46.880Z
Answers.UserDisplayName: Maxim
Body: <p>I  recently became interested in how creativity is generated in NN. My understanding of NNs is that the output always known, given that it they are trained with target values, but how does one train a network to be creative, I mean in such cases would the task be to create something novel, but the actual target would not be known.  How does creativity express itself in a rule based system, such as NN?</p>
  
  <p>One would need to redefine the cost-function, but how does one imply creativity in a rule based system?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-10-11T18:22:58.917Z
Comments.Text: Great subject.  For further research, I'd recommend looking at the [computational creativity wiki](https://en.wikipedia.org/wiki/Computational_creativity) and the cited papers and publications.  You may also want to look into how [monte-carlo tree search](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search) in Machine Learning algorithms.
Comments.UserDisplayName: DukeZhou
Tags: <ai-design>
Title: How is creativity generated in a currently rule based neural network?
UserDisplayName: Carlton Banks
ThreadText: # How is creativity generated in a currently rule based neural network?
  
  (Asked by: username_0 on 2017-10-09)
  
  I recently became interested in how creativity is generated in NN. My understanding of NNs is that the output always known, given that it they are trained with target values, but how does one train a network to be creative, I mean in such cases would the task be to create something novel, but the actual target would not be known. How does creativity express itself in a rule based system, such as NN?
  
  One would need to redefine the cost-function, but how does one imply creativity in a rule based system?
  
  **Tags:** ai-design
  > **Comments:**
  > **username_1**: Great subject.  For further research, I'd recommend looking at the [computational creativity wiki](https://en.wikipedia.org/wiki/Computational_creativity) and the cited papers and publications.  You may also want to look into how [monte-carlo tree search](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search) in Machine Learning algorithms.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2017-10-11. Score: 2 | Accepted answer
  
  Your question is a bit broad. Some examples of creativity and how it's achieved (in all of them there is an ordinary convolutional network trained via supervised learning, but applied differently):
  
  * Google Deep Dream uses [backpropagation to the image](https://cs.stackexchange.com/questions/56406/google-deepdream-elaborated/82074) to tweak the image towards maximum activation in the convolutional layer. As a result, it finds lots of "things" in the image and enhances them:
  
    [![deep-dream](https://i.sstatic.net/0Hrgx.jpg)](https://i.sstatic.net/0Hrgx.jpg)
  * [Generative Adversarial Networks](http://kvfrans.com/generative-adversial-networks-explained/) use two competing networks (generator and discriminator) to generate samples from the training manifold:
  
    [![generated-bedrooms](https://i.sstatic.net/tDGXt.jpg)](https://i.sstatic.net/tDGXt.jpg)
  * [Neuro style](https://github.com/jcjohnson/neural-style) method captures the style of one image via Gram matrix of convolutional layer activations and trains (another) neural network with a special loss function that minimizes the joint distance to the incoming image (content) and to the Gram matrix (style). The result:
  
    [![neuro-style](https://i.sstatic.net/8LbQk.jpg)](https://i.sstatic.net/8LbQk.jpg)
  
  > **Comments:**
  > **username_0**: Thanks for the reply.. And for giving this overall reply.. It surely helps with the understanding, or what too google for too for my further investigations.  
  
  ---
  
  