Answers.Body: <p>You usually don't want to do so, in order to avoid shooting at your own foot.</p>
  <p>Indeed, point <span class="math-container">$6$</span> updates the parameter of the discriminator, therefore it should be &quot;pretty good&quot; on the generated images used for that SGD update, and thus if you then update the generator, you risk to have a bad signal (potentially, you would like to have a gradient that has &quot;generalized&quot;, but if you reuse the samples you risk to have a sort of overfit-gradient more than a generalized-gradient)</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-04-01T11:14:13.703Z
Answers.LastActivityDate: 2024-04-01T11:14:13.703Z
Answers.UserDisplayName: Alberto
Body: <p>I don't understand the necessity of the redundance in the training of DCGAN. So a classical DSGAN training procedure is like this:
  <a href="https://i.sstatic.net/jOVAv.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/jOVAv.png" alt="enter image description here" /></a>
  My questions:</p>
  <ol>
  <li>Can I remove step (7)-(8) and and reuse the fake images of (4) to compute the generator loss?</li>
  <li>If so, can I further reuse the result of <span class="math-container">$D(G(z))$</span>? i.e.
  Generate fake images -&gt; compute generator loss and discriminator loss using the same <span class="math-container">$D(G(z))$</span> values -&gt; update generator -&gt; update discriminator</li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-02T15:02:46.563Z
Comments.Text: Instead of writing "Question about the redundance..." in the title, can you please just write your specific question there?
Comments.UserDisplayName: nbro
Tags: <generative-adversarial-networks>
Title: Question about the redundance in DCGAN training
UserDisplayName: lovetl2002
ThreadText: # Question about the redundance in DCGAN training
  
  (Asked by: username_0 on 2024-04-01)
  
  I don't understand the necessity of the redundance in the training of DCGAN. So a classical DSGAN training procedure is like this:
  [![enter image description here](https://i.sstatic.net/jOVAv.png)](https://i.sstatic.net/jOVAv.png)
  My questions:
  
  1. Can I remove step (7)-(8) and and reuse the fake images of (4) to compute the generator loss?
  2. If so, can I further reuse the result of $D(G(z))$? i.e.
     Generate fake images -> compute generator loss and discriminator loss using the same $D(G(z))$ values -> update generator -> update discriminator
  
  **Tags:** generative-adversarial-networks
  
  > **Comments:**
  > **username_1**: Instead of writing "Question about the redundance..." in the title, can you please just write your specific question there?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2024-04-01. Score: 0
  
  You usually don't want to do so, in order to avoid shooting at your own foot.
  
  Indeed, point $6$ updates the parameter of the discriminator, therefore it should be "pretty good" on the generated images used for that SGD update, and thus if you then update the generator, you risk to have a bad signal (potentially, you would like to have a gradient that has "generalized", but if you reuse the samples you risk to have a sort of overfit-gradient more than a generalized-gradient)
  
  
  ---
  
  