Answers.Body: <p>You can encode this in a loss, not in a differentiable one though.</p>
  <p>What you want is something like:
  <span class="math-container">$$
  L(\theta) = L^{VAE}(\theta) - \lambda \sum_i|set(f_\theta(x_i))|
  $$</span>
  So you penalize your model if the size of the set of colors used in the prediction is too big... clearly, this is not differentiable</p>
  <p>What you can do is the following:</p>
  <ul>
  <li>discretize it post processing : indeed, you will have a &quot;histogram&quot; of color distribution, create an heuristics that reflects your tastes</li>
  <li>discretize it as pre-processing, thus define a set of colors you allow, so instead of <span class="math-container">$[0,255]^3$</span> you pick <span class="math-container">$\{n\%16 == 0 | 0 \le n \le 255\}^3$</span>, adn then to deploy the model, just let it predict a color, and set it to the closest one of your palette</li>
  </ul>
  <p>You can also do a sort of RL reinforce for the color, so you get your prediction, you bin the colors, and you use RL to reinforce the one you prefer, however this  involves RL, which is a world of his own</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-11-20T21:47:04.723Z
Answers.Text: Thanks so much for your help! I think I will attempt your main recommendation as  it should fill the current needs of the project. Just to confirm because I'm quite new to ML as a whole, I should first get a set amount of colors that the model is allowed to use and store them in the histogram; then after the image is generated, instead of using a loss function to punish it for using a wide variety of colors, I will instead just convert every color to the closest match that is on the histogram.
Answers.UserDisplayName: Max
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-11-20T21:54:58.010Z
Answers.Text: @Max yes, however, you first have to train your VAE, and then at "evaluation" you do that, so the training is the usual VAE training (otherwise doing the adjustment will cause to loose the gradient)
Answers.UserDisplayName: Alberto
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-11-20T21:03:58.060Z
Answers.LastActivityDate: 2023-11-20T21:03:58.060Z
Answers.UserDisplayName: Alberto
Body: <p>So recently I have been trying to program a Tensorflow and Keras based model that can animate pixel art characters. I use a Variation Auto Encoder with Convolutions, Dense Layers and Upsampling 2d layers. One of the major problems that I have been facing is that the generated images tend to be very noisy and complex when it comes to color selection. Normally this would be fine for more realistic images but I'm aiming for more of a pixel art and voxel look where the produced image uses a more restricted color palette. My model intakes 2 (48,48) RBGA image and outputs a single (48,48) RBGA image that acts as an interpolation between the two.</p>
  <p>Does anyone know of an optimizer, loss function, special layer or some other trick to potentially limit or disencourage the model from using a lot of different types of colors. I'm considering making a custom layer that encodes each color within the input images to a set integer and have the model predict which integers to put where. This would result in the model only being able to use colors that showed up within the input images but has 2 major issues. First of all this would severely restrict the models creativity as it can't use new colors when it should. Another major issue with this is that it would remove the models ability to understand how different colors should interact with each other within the Convolutions.</p>
  <p>TLDR: I want to punish my Variatonal Autoencoder image generation model more for using a lot of different colors or reward the model for using less colors while still letting it make creative decisions and understand the relationships between colors.</p>
  <p>Target Image:</p>
  <p><a href="https://i.sstatic.net/Q1mQR.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Q1mQR.png" alt="Target Image:" /></a></p>
  <p>Generated Image:</p>
  <p><a href="https://i.sstatic.net/5mDg6m.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/5mDg6m.png" alt="Generated Image" /></a></p>
  <p>I was told to move this problem from Stack overflow due to it being primarily conceptual; here's the link to the old problem/discussion: <a href="https://stackoverflow.com/questions/77499873/is-there-a-way-to-reward-my-variational-auto-encoder-for-using-less-colors-while">Old Question</a></p>
  
Tags: <variational-autoencoder><image-generation>
Title: Is there a way to reward my Variational-Auto encoder for using less colors while still letting it make creative decisions?
UserDisplayName: Max
ThreadText: # Is there a way to reward my Variational-Auto encoder for using less colors while still letting it make creative decisions?
  
  (Asked by: username_0 on 2023-11-20)
  
  So recently I have been trying to program a Tensorflow and Keras based model that can animate pixel art characters. I use a Variation Auto Encoder with Convolutions, Dense Layers and Upsampling 2d layers. One of the major problems that I have been facing is that the generated images tend to be very noisy and complex when it comes to color selection. Normally this would be fine for more realistic images but I'm aiming for more of a pixel art and voxel look where the produced image uses a more restricted color palette. My model intakes 2 (48,48) RBGA image and outputs a single (48,48) RBGA image that acts as an interpolation between the two.
  
  Does anyone know of an optimizer, loss function, special layer or some other trick to potentially limit or disencourage the model from using a lot of different types of colors. I'm considering making a custom layer that encodes each color within the input images to a set integer and have the model predict which integers to put where. This would result in the model only being able to use colors that showed up within the input images but has 2 major issues. First of all this would severely restrict the models creativity as it can't use new colors when it should. Another major issue with this is that it would remove the models ability to understand how different colors should interact with each other within the Convolutions.
  
  TLDR: I want to punish my Variatonal Autoencoder image generation model more for using a lot of different colors or reward the model for using less colors while still letting it make creative decisions and understand the relationships between colors.
  
  Target Image:
  
  [![Target Image:](https://i.sstatic.net/Q1mQR.png)](https://i.sstatic.net/Q1mQR.png)
  
  Generated Image:
  
  [![Generated Image](https://i.sstatic.net/5mDg6m.png)](https://i.sstatic.net/5mDg6m.png)
  
  I was told to move this problem from Stack overflow due to it being primarily conceptual; here's the link to the old problem/discussion: [Old Question](https://stackoverflow.com/questions/77499873/is-there-a-way-to-reward-my-variational-auto-encoder-for-using-less-colors-while)
  
  **Tags:** variational-autoencoder, image-generation
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-11-20. Score: 0 | Accepted answer
  
  You can encode this in a loss, not in a differentiable one though.
  
  What you want is something like:
  $$
  L(\theta) = L^{VAE}(\theta) - \lambda \sum\_i|set(f\_\theta(x\_i))|
  $$
  So you penalize your model if the size of the set of colors used in the prediction is too big... clearly, this is not differentiable
  
  What you can do is the following:
  
  * discretize it post processing : indeed, you will have a "histogram" of color distribution, create an heuristics that reflects your tastes
  * discretize it as pre-processing, thus define a set of colors you allow, so instead of $[0,255]^3$ you pick $\{n\%16 == 0 | 0 \le n \le 255\}^3$, adn then to deploy the model, just let it predict a color, and set it to the closest one of your palette
  
  You can also do a sort of RL reinforce for the color, so you get your prediction, you bin the colors, and you use RL to reinforce the one you prefer, however this involves RL, which is a world of his own
  
  > **Comments:**
  > **username_0**: Thanks so much for your help! I think I will attempt your main recommendation as  it should fill the current needs of the project. Just to confirm because I'm quite new to ML as a whole, I should first get a set amount of colors that the model is allowed to use and store them in the histogram; then after the image is generated, instead of using a loss function to punish it for using a wide variety of colors, I will instead just convert every color to the closest match that is on the histogram.  
  > **username_1**: @username_0 yes, however, you first have to train your VAE, and then at "evaluation" you do that, so the training is the usual VAE training (otherwise doing the adjustment will cause to loose the gradient)  
  
  ---
  
  