Body: <p>Generative Adversarial Networks, in general, consists of two multi layer perceptrons: generator and discriminator. Generator is used for generating samples that are as real as training samples and discriminator tries to discriminate the real and fake samples.</p>
  <p>Generator receives noise and generate samples.</p>
  <p>Some papers like <a href="https://arxiv.org/pdf/1605.05396.pdf#page=5" rel="nofollow noreferrer">this</a> use extra networks</p>
  <blockquote>
  <p>To achieve this, <strong>one can train a convolutional network to invert
  <span class="math-container">$G$</span></strong> to regress from samples <span class="math-container">$\hat{x} \leftarrow G(z, \phi(t))$</span> back
  onto <span class="math-container">$z$</span>.</p>
  </blockquote>
  <p>And some other papers, I remember saying that we use the same generator architecture to invert the it.</p>
  <p>What does it mean by inverting the generator of a GAN? Does inverting means passing samples and getting noise as output by using same or different architectures?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-09T06:15:56.503Z
Comments.Text: Can you provide a link and/or quote where the term is used? I have studied GANs and not heard of anyone use the phrase "inverting the generator".
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-09T07:20:12.580Z
Comments.Text: Oh, sorry. [4.4. Inverting the generator for style transfer](https://arxiv.org/pdf/1605.05396.pdf#page=5)
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-09T07:23:20.677Z
Comments.Text: OK, that does look correct, but I don't understand what the problem is, that short section explains *excatly* what inverting is for that secnario, including the loss function. Where are you stuck in your understanding?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-09T07:24:48.037Z
Comments.Text: @NeilSlater I came across multiple ways of doing so. Here, I feel that they are not actually inverting the generator. They are inverting via another network. So, I want to know when can we say that "we are inverting the generator of GAN". Is it by using another network or as I mentioned in the question or both. I mean "is there any standard definition for inverting a generator in GAN?".
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-09T07:28:32.097Z
Comments.Text: In that case you will need multiple links to show the "multiple ways" that you have found, and explain this issue in the question. That is because "inverting the generator" is not a basic concept of GANs - it is not something that can be looked up or referenced. Actually I doubt it is a standard term at all, but if you explain in more detail in the question *why* you have conflicting views of what the term means, perhaps someone who is more of an expert with GANs can help
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-09T07:33:37.267Z
Comments.Text: If I were to guess, I would say that "inverting the generator" is creating a function that takes an image as input and outputs a vector that would create a similar image if used as input to the generator of the GAN. That is what is happening in the linked paper. Any differences will then be in the approaches used to create that inversion function. It is generally not possible to directly invert a feed-forward network by "running it backwards" - some kind of itertive process or training process will be required, or a different starting architecture.
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-09T07:43:30.470Z
Comments.Text: @NeilSlater Oh! then I will try to edit today only.
Comments.UserDisplayName: hanugm
Tags: <generative-adversarial-networks><generator>
Title: What is meant by inverting the generator?
UserDisplayName: hanugm
ThreadText: # What is meant by inverting the generator?
  
  (Asked by: username_0 on 2021-09-09)
  
  Generative Adversarial Networks, in general, consists of two multi layer perceptrons: generator and discriminator. Generator is used for generating samples that are as real as training samples and discriminator tries to discriminate the real and fake samples.
  
  Generator receives noise and generate samples.
  
  Some papers like [this](https://arxiv.org/pdf/1605.05396.pdf#page=5) use extra networks
  
  > To achieve this, **one can train a convolutional network to invert
  > $G$** to regress from samples $\hat{x} \leftarrow G(z, \phi(t))$ back
  > onto $z$.
  
  And some other papers, I remember saying that we use the same generator architecture to invert the it.
  
  What does it mean by inverting the generator of a GAN? Does inverting means passing samples and getting noise as output by using same or different architectures?
  
  **Tags:** generative-adversarial-networks, generator
  > **Comments:**
  > **username_1**: Can you provide a link and/or quote where the term is used? I have studied GANs and not heard of anyone use the phrase "inverting the generator".  
  > **username_0**: Oh, sorry. [4.4. Inverting the generator for style transfer](https://arxiv.org/pdf/1605.05396.pdf#page=5)  
  > **username_1**: OK, that does look correct, but I don't understand what the problem is, that short section explains *excatly* what inverting is for that secnario, including the loss function. Where are you stuck in your understanding?  
  > **username_0**: @NeilSlater I came across multiple ways of doing so. Here, I feel that they are not actually inverting the generator. They are inverting via another network. So, I want to know when can we say that "we are inverting the generator of GAN". Is it by using another network or as I mentioned in the question or both. I mean "is there any standard definition for inverting a generator in GAN?".  
  > **username_1**: In that case you will need multiple links to show the "multiple ways" that you have found, and explain this issue in the question. That is because "inverting the generator" is not a basic concept of GANs - it is not something that can be looked up or referenced. Actually I doubt it is a standard term at all, but if you explain in more detail in the question *why* you have conflicting views of what the term means, perhaps someone who is more of an expert with GANs can help  
  > **username_1**: If I were to guess, I would say that "inverting the generator" is creating a function that takes an image as input and outputs a vector that would create a similar image if used as input to the generator of the GAN. That is what is happening in the linked paper. Any differences will then be in the approaches used to create that inversion function. It is generally not possible to directly invert a feed-forward network by "running it backwards" - some kind of itertive process or training process will be required, or a different starting architecture.  
  > **username_0**: @NeilSlater Oh! then I will try to edit today only.  
  
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  ## 0 answers
  
  