Answers.Body: <p>Although there is no definitive answer for what you are asking, I can offer my 2 cents on what I would do in your situation.</p>
  <ol>
  <li>With sparse data, you first want to check if you can compress your sparse inputs into something less sparse. You want this compression to be reversible, trivially. Compression (or dimensionality reduction) will help your downstream task of generation easier, as you will not have to deal (or have to deal less) with the difficult sparsity. For compression/dimensionality reduction, you will have a couple options:
  <ol>
  <li>I'm not very familiar with Fourier transforms, but knowing their importance in all of science, I would not be surprised if there is some kind of reversible compression technique. I do not have any knowledge of this though. Maybe it doesn't exist.</li>
  <li>There are many dimensionality reduction techniques that use neural nets, such as SVD, PCA, or autoencoders. You can research what you would prefer in this scenario.</li>
  </ol>
  </li>
  <li>If you want to improve your model for sparse data, it is often preferable to have some <a href="https://stats.stackexchange.com/questions/45643/why-l1-norm-for-sparse-models">L1 loss or normalization component in your architecture</a>. This will help to model sparsity.</li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-10-07T12:17:03.520Z
Answers.LastActivityDate: 2023-10-07T12:17:03.520Z
Answers.UserDisplayName: Robin van Hoorn
Body: <p>I want to modify WGAN-gp so that it will work on very sparse 1D data (FFT of Gauss signal). Can you suggest any methods or papers that will be helpful?
  I have a working WGAN-gp, but it is not effective at all.</p>
  <p>Here is the code of my model: <a href="https://pastecode.io/s/1u9fx4te" rel="nofollow noreferrer">https://pastecode.io/s/1u9fx4te</a></p>
  <p><a href="https://i.sstatic.net/xqQGM.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/xqQGM.png" alt="enter image description here" /></a></p>
  <p>This is the plot of a typical data example.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-05T11:22:05.517Z
Comments.Text: Hi! Welcome to AI SE. Can you elaborate a bit more on the form of your data? Is it temporal data? What kind of dimensions are we speaking of?
Comments.UserDisplayName: Robin van Hoorn
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-05T13:54:29.787Z
Comments.Text: I have vectors of shape [1824,1]. data consists of Fourier Transformations of Gauss signals. I added plot of data to the question
Comments.UserDisplayName: chrzanowski
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-07T12:16:31.327Z
Comments.Text: I'm still not 100% sure what is meant by 'fourier tranformations of gauss signals'. This is not a 'standard type of data' that i am familiar with. If you want very specific help, you will need to be much more specific on how this data is created etc. Do not assume people will understand the terms that you have gotten familiar with through your project. Nonetheless, i added some suggestions in an answer to help you get started.
Comments.UserDisplayName: Robin van Hoorn
Tags: <generative-adversarial-networks>
Title: How to deal with sparse 1D data with WGAN-gp
UserDisplayName: chrzanowski
ThreadText: # How to deal with sparse 1D data with WGAN-gp
  
  (Asked by: username_0 on 2023-10-05)
  
  I want to modify WGAN-gp so that it will work on very sparse 1D data (FFT of Gauss signal). Can you suggest any methods or papers that will be helpful?
  I have a working WGAN-gp, but it is not effective at all.
  
  Here is the code of my model: <https://pastecode.io/s/1u9fx4te>
  
  [![enter image description here](https://i.sstatic.net/xqQGM.png)](https://i.sstatic.net/xqQGM.png)
  
  This is the plot of a typical data example.
  
  **Tags:** generative-adversarial-networks
  > **Comments:**
  > **username_1**: Hi! Welcome to AI SE. Can you elaborate a bit more on the form of your data? Is it temporal data? What kind of dimensions are we speaking of?  
  > **username_0**: I have vectors of shape [1824,1]. data consists of Fourier Transformations of Gauss signals. I added plot of data to the question  
  > **username_1**: I'm still not 100% sure what is meant by 'fourier tranformations of gauss signals'. This is not a 'standard type of data' that i am familiar with. If you want very specific help, you will need to be much more specific on how this data is created etc. Do not assume people will understand the terms that you have gotten familiar with through your project. Nonetheless, i added some suggestions in an answer to help you get started.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-10-07. Score: 1
  
  Although there is no definitive answer for what you are asking, I can offer my 2 cents on what I would do in your situation.
  
  1. With sparse data, you first want to check if you can compress your sparse inputs into something less sparse. You want this compression to be reversible, trivially. Compression (or dimensionality reduction) will help your downstream task of generation easier, as you will not have to deal (or have to deal less) with the difficult sparsity. For compression/dimensionality reduction, you will have a couple options:
     1. I'm not very familiar with Fourier transforms, but knowing their importance in all of science, I would not be surprised if there is some kind of reversible compression technique. I do not have any knowledge of this though. Maybe it doesn't exist.
     2. There are many dimensionality reduction techniques that use neural nets, such as SVD, PCA, or autoencoders. You can research what you would prefer in this scenario.
  2. If you want to improve your model for sparse data, it is often preferable to have some [L1 loss or normalization component in your architecture](https://stats.stackexchange.com/questions/45643/why-l1-norm-for-sparse-models). This will help to model sparsity.
  
  
  ---
  
  