Answers.Body: <p>In my experience, it's not a matter of performance benefits; Variational Auto-Encoder GANs are much more useful if you want to have &quot;knobs&quot; to turn to influence the generated output.  Since you have a latent layer that represents possibly the mean and the distribution of the data, you can tune to different &quot;positions&quot; in that latent space to influence the output of the GAN.</p>
  <p>Without this, the generated output is more difficult to predict and/or you will end up with some outputs that are great and others that are completely meaningless.</p>
  
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Answers.CreationDate: 2022-04-12T00:33:31.280Z
Answers.Text: Isnt this what a StyleGan effectively achieves too though?
Answers.UserDisplayName: mesllo
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Answers.CreationDate: 2021-06-01T18:56:01.777Z
Answers.LastActivityDate: 2021-06-01T22:57:59.087Z
Answers.LastEditDate: 2021-06-01T22:57:59.087Z
Answers.UserDisplayName: David Hoelzer
Body: <p>I have read that when using VAE-GANs, first what happens is the VAE's encoder encodes some image to another encoded image, which from GAN's point of view is considered a noise, and then the GAN part generates another image from that noise which from VAE's point of view is just an encoded image.</p>
  <p>Is that encoded image better suited for GAN to generate better images or not?</p>
  <p>The problem which bugs me is that there are not that many articles about VAE-GANs, especially in the last 2 years.</p>
  <p>As a side question, does that mean that VAE-GANs do not have any significant performance benefits than just simple GAN?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-06-03T14:48:47.923Z
Comments.Text: I'm not sure about Variational AE, but take a look at CycleGAN. The generator serves as AE. It takes an image as input and the first part of the network is an encoder followed by 9 ResBlocks. The last part is a decoder. The authors do not use random noise. Instead, features of an image an extracted with an encoder and used to generate a new image. The same can be done for StyleGAN. It is possible to encode a style of an image and pass it to the generator.
Comments.UserDisplayName: Aray Karjauv
Tags: <deep-learning><generative-adversarial-networks><variational-autoencoder>
Title: Is there a performace benefits using VAE-GAN instead of just GAN?
UserDisplayName: artas2357
ThreadText: # Is there a performace benefits using VAE-GAN instead of just GAN?
  
  (Asked by: username_0 on 2021-06-01)
  
  I have read that when using VAE-GANs, first what happens is the VAE's encoder encodes some image to another encoded image, which from GAN's point of view is considered a noise, and then the GAN part generates another image from that noise which from VAE's point of view is just an encoded image.
  
  Is that encoded image better suited for GAN to generate better images or not?
  
  The problem which bugs me is that there are not that many articles about VAE-GANs, especially in the last 2 years.
  
  As a side question, does that mean that VAE-GANs do not have any significant performance benefits than just simple GAN?
  
  **Tags:** deep-learning, generative-adversarial-networks, variational-autoencoder
  
  > **Comments:**
  > **username_1**: I'm not sure about Variational AE, but take a look at CycleGAN. The generator serves as AE. It takes an image as input and the first part of the network is an encoder followed by 9 ResBlocks. The last part is a decoder. The authors do not use random noise. Instead, features of an image an extracted with an encoder and used to generate a new image. The same can be done for StyleGAN. It is possible to encode a style of an image and pass it to the generator.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2021-06-01. Score: 1
  
  In my experience, it's not a matter of performance benefits; Variational Auto-Encoder GANs are much more useful if you want to have "knobs" to turn to influence the generated output. Since you have a latent layer that represents possibly the mean and the distribution of the data, you can tune to different "positions" in that latent space to influence the output of the GAN.
  
  Without this, the generated output is more difficult to predict and/or you will end up with some outputs that are great and others that are completely meaningless.
  
  > **Comments:**
  > **username_3**: Isnt this what a StyleGan effectively achieves too though?  
  
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