Body: <p>In the research paper titled <a href="https://arxiv.org/pdf/1411.1784.pdf" rel="nofollow noreferrer">Conditional Generative Adversarial Nets</a> by <em>Mehdi Mirza and Simon Osindero</em>, there is a notion of conditioning a neural network on a class label.</p>
  <p>It is mentioned in the abstract that we need to <strong>simply</strong> feed extra input <span class="math-container">$y$</span> to the generator and discriminator of an unconditional GAN.</p>
  <blockquote>
  <p>Generative Adversarial Nets were recently introduced as a novel
  way to train generative models. In this work we introduce the
  conditional version of generative adversarial nets, which can be
  <strong>constructed by simply feeding the data, <span class="math-container">$y$</span>, we wish to condition on to
  both the generator and discriminator.</strong> We show that this model can
  generate MNIST digits conditioned on class labels. We also illustrate
  how this model could be used to learn a multi-modal model, and provide
  preliminary examples of an application to image tagging in which we
  demonstrate how this approach can generate descriptive tags which are
  not part of training labels.</p>
  </blockquote>
  <p>So, I cannot see whether there is any special treatment for input <span class="math-container">$y$</span>.</p>
  <p>If there is no special treatment for the data <span class="math-container">$y$</span>, then why do they call <span class="math-container">$y$</span> a condition and follow the notation of conditional probability such as <span class="math-container">$G(z|y), D(x|y)$</span> instead of <span class="math-container">$G(z,y), D(x,y)$</span>?</p>
  <p>If there is a special treatment to input <span class="math-container">$y$</span>, then what is that special? Don't they pass <span class="math-container">$y$</span> in the same way as <span class="math-container">$x$</span> to the neural networks?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-05T10:35:36.023Z
Comments.Text: Normally data is *not* fed to both the generator and the discriminator, so isn't that already a special treatment?
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-07T22:32:04.600Z
Comments.Text: @user253751 yeah true. In general $z$ is the only input to generator and $x$ or $G(z)$  is the actual input to discriminator. Please answer it if you are sure that $y$ has no difference in treatment as $z$ for generator and $y$ has no difference in treatment as $x$ or $G(z)$ for discriminator. But, how would they pass $y$ is another doubt in my question.  Will they pass normally? Suppose $z=[1, 2, 3, 4]$ and $y = [1, 0, 0, 0, 0]$ then what can be the actual input for $G$?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-07T22:33:10.687Z
Comments.Text: @user253751 why can't they use $G(z, y)$ instead of  $G(z/y)$ etc.,?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-08T20:33:20.923Z
Comments.Text: they can, or they can not. It's their choice since they are the ones writing the letters.
Comments.UserDisplayName: Criticize SE actions means ban
Tags: <generative-adversarial-networks><notation><input-layer><conditional-probability><conditional-gan>
Title: Is there any difference between 'input' and 'conditional input' in the case of neural networks?
UserDisplayName: hanugm
ThreadText: # Is there any difference between 'input' and 'conditional input' in the case of neural networks?
  
  (Asked by: username_0 on 2021-08-04)
  
  In the research paper titled [Conditional Generative Adversarial Nets](https://arxiv.org/pdf/1411.1784.pdf) by *Mehdi Mirza and Simon Osindero*, there is a notion of conditioning a neural network on a class label.
  
  It is mentioned in the abstract that we need to **simply** feed extra input $y$ to the generator and discriminator of an unconditional GAN.
  
  > Generative Adversarial Nets were recently introduced as a novel
  > way to train generative models. In this work we introduce the
  > conditional version of generative adversarial nets, which can be
  > **constructed by simply feeding the data, $y$, we wish to condition on to
  > both the generator and discriminator.** We show that this model can
  > generate MNIST digits conditioned on class labels. We also illustrate
  > how this model could be used to learn a multi-modal model, and provide
  > preliminary examples of an application to image tagging in which we
  > demonstrate how this approach can generate descriptive tags which are
  > not part of training labels.
  
  So, I cannot see whether there is any special treatment for input $y$.
  
  If there is no special treatment for the data $y$, then why do they call $y$ a condition and follow the notation of conditional probability such as $G(z|y), D(x|y)$ instead of $G(z,y), D(x,y)$?
  
  If there is a special treatment to input $y$, then what is that special? Don't they pass $y$ in the same way as $x$ to the neural networks?
  
  **Tags:** generative-adversarial-networks, notation, input-layer, conditional-probability, conditional-gan
  > **Comments:**
  > **username_1**: Normally data is *not* fed to both the generator and the discriminator, so isn't that already a special treatment?  
  > **username_0**: @user253751 yeah true. In general $z$ is the only input to generator and $x$ or $G(z)$  is the actual input to discriminator. Please answer it if you are sure that $y$ has no difference in treatment as $z$ for generator and $y$ has no difference in treatment as $x$ or $G(z)$ for discriminator. But, how would they pass $y$ is another doubt in my question.  Will they pass normally? Suppose $z=[1, 2, 3, 4]$ and $y = [1, 0, 0, 0, 0]$ then what can be the actual input for $G$?  
  > **username_0**: @user253751 why can't they use $G(z, y)$ instead of  $G(z/y)$ etc.,?  
  > **username_1**: they can, or they can not. It's their choice since they are the ones writing the letters.  
  
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  ## 0 answers
  
  