Answers.Body: <p>Due to subjective nature, quantitative evaluation of synthetic images is difficult in general. However, there are metrics like <a href="https://arxiv.org/abs/1606.03498" rel="nofollow noreferrer"><strong>Inception Score</strong></a> or <a href="https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance" rel="nofollow noreferrer"><strong>FID score</strong></a> that are used for evaluation of generative models like GANs or VAEs. Technically, it considers two aspects of the generated data:</p>
  <ol>
  <li>Similarity with training data</li>
  <li>Diversity within itself</li>
  </ol>
  <p>Even though such metrics do not assess new images as we humans do, but it is widely accepted in the community.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-10-12T19:44:11.323Z
Answers.LastActivityDate: 2021-10-12T19:44:11.323Z
Answers.UserDisplayName: ayandas
Body: <p>I'm working on a VAE model to produce synthetic data of X-Ray diffraction spectrums.</p>
  <p>I try to figure out how I can measure the quality of the spectrums. The goal would be to produce synthetic data which is similar to the training data but also different from the training data. The spectrums should keep their characteristics, but should be different in terms of noise and intensity.</p>
  <p>I trained models which can produce those type of spectrums (because I checked some of them visual), but I don't know how to quantify the difference/similarity to the origin (1) and the difference between the produced synthetic spectrums in one dataset (2).</p>
  <p>Are there any methods to quantify these points?</p>
  
Tags: <datasets><autoencoders><generative-model><variational-autoencoder><algorithm-request>
Title: How to determine the quality of synthetic data?
UserDisplayName: Tekay
ThreadText: # How to determine the quality of synthetic data?
  
  (Asked by: username_0 on 2021-10-08)
  
  I'm working on a VAE model to produce synthetic data of X-Ray diffraction spectrums.
  
  I try to figure out how I can measure the quality of the spectrums. The goal would be to produce synthetic data which is similar to the training data but also different from the training data. The spectrums should keep their characteristics, but should be different in terms of noise and intensity.
  
  I trained models which can produce those type of spectrums (because I checked some of them visual), but I don't know how to quantify the difference/similarity to the origin (1) and the difference between the produced synthetic spectrums in one dataset (2).
  
  Are there any methods to quantify these points?
  
  **Tags:** datasets, autoencoders, generative-model, variational-autoencoder, algorithm-request
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-10-12. Score: 4 | Accepted answer
  
  Due to subjective nature, quantitative evaluation of synthetic images is difficult in general. However, there are metrics like [**Inception Score**](https://arxiv.org/abs/1606.03498) or [**FID score**](https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance) that are used for evaluation of generative models like GANs or VAEs. Technically, it considers two aspects of the generated data:
  
  1. Similarity with training data
  2. Diversity within itself
  
  Even though such metrics do not assess new images as we humans do, but it is widely accepted in the community.
  
  
  ---
  
  