Answers.Body: <p>Simply, it is just a design choice.</p>
  <p>Isotropic gaussian is one of the easiest density to work with. It has an easy-to-compute likelihood and easily reparameterizable.</p>
  <p>You are free to use other distribution, but might face computational or implementation hurdles.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-10-04T11:50:10.000Z
Answers.LastActivityDate: 2021-10-04T11:50:10.000Z
Answers.UserDisplayName: ayandas
Body: <p>While training a standard VAE, we assume that the prior on the latent variable Z is the standard gaussian and we use KL divergence to push the posterior as close as possible to the standard gaussian. Why not assume any other gaussian as the prior? What are the intuitive reasons for this?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-25T13:30:36.813Z
Comments.Text: A good reason is we normalize data before the training process!
Comments.UserDisplayName: OmG
Tags: <deep-learning><autoencoders><variational-autoencoder>
Title: Why is the prior on the latent variable standard gaussian in VAE?
UserDisplayName: Manish Kausik Hari Baskar
ThreadText: # Why is the prior on the latent variable standard gaussian in VAE?
  
  (Asked by: username_0 on 2021-08-25)
  
  While training a standard VAE, we assume that the prior on the latent variable Z is the standard gaussian and we use KL divergence to push the posterior as close as possible to the standard gaussian. Why not assume any other gaussian as the prior? What are the intuitive reasons for this?
  
  **Tags:** deep-learning, autoencoders, variational-autoencoder
  > **Comments:**
  > **username_1**: A good reason is we normalize data before the training process!  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2021-10-04. Score: 2
  
  Simply, it is just a design choice.
  
  Isotropic gaussian is one of the easiest density to work with. It has an easy-to-compute likelihood and easily reparameterizable.
  
  You are free to use other distribution, but might face computational or implementation hurdles.
  
  
  ---
  
  