Answers.Body: <p>The &quot;Helvetica scenario&quot; referenced in the paper is an AI issue related to generative systems failing.</p>
  <p>In GANs, this might be due to a generator becoming initially strong against the discriminator by focusing on its best output results so far that cover just a subset of the full dataset - i.e. gradients move the generator towards output of a single best class, suppressing output of classes that the discriminator can spot easily, and encouraging output of classes where the discriminator has more trouble. By the time the discriminator catches up with the generator, it may be too late for the combined training system to learn its way out of this state.</p>
  <p>In GANs, the term <em>Helvetica scenario</em> from the paper is more commonly referred to as <em>mode collapse</em> in later literature. If you search for &quot;GAN mode collapse&quot; you will find a lot more information about it. There are a few different improvements to GAN design - loss functions and other hyperparameters - that work to reduce the incidence of mode collapse.</p>
  <hr />
  <p>From <em><a href="https://datascience.stackexchange.com/questions/49744/where-can-i-find-out-about-the-helvetica-scenario">this answer</a></em> on Data Science SE, it seems like the authors named the effect after a science parody sketch in the show Look Around You, where the structure of a molecule collapses. The &quot;science&quot; in the sketch is complete nonsense.</p>
  
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Answers.CreationDate: 2022-05-18T17:57:02.617Z
Answers.LastActivityDate: 2022-05-18T18:52:58.083Z
Answers.LastEditDate: 2022-05-18T18:52:58.083Z
Answers.UserDisplayName: Neil Slater
Body: <p>Consider the following sentence from the original GAN paper titled <a href="https://arxiv.org/pdf/1406.2661.pdf#page=7" rel="nofollow noreferrer">Generative Adversarial Nets</a></p>
  <blockquote>
  <p>in particular, <span class="math-container">$G$</span> must not be trained too much without updating <span class="math-container">$D$</span>, in order to avoid &quot;the Helvetica scenario&quot; in which <span class="math-container">$G$</span> collapses too many values of <span class="math-container">$\mathbf{z}$</span> to the same value of <span class="math-container">$\mathbf{x}$</span> to have enough diversity to model <span class="math-container">$p_{\text {data }}$</span></p>
  </blockquote>
  <p>The sentence uses the name of a scenario called Helvetica. Is it related to AI or chemistry? If AI, then where can I read more about the scenario?</p>
  
Tags: <terminology><papers><generative-adversarial-networks>
Title: Is the "Helvetica scenario" mentioned here related to Artificial Intelligence?
UserDisplayName: hanugm
ThreadText: # Is the "Helvetica scenario" mentioned here related to Artificial Intelligence?
  
  (Asked by: username_0 on 2022-05-18)
  
  Consider the following sentence from the original GAN paper titled [Generative Adversarial Nets](https://arxiv.org/pdf/1406.2661.pdf#page=7)
  
  > in particular, $G$ must not be trained too much without updating $D$, in order to avoid "the Helvetica scenario" in which $G$ collapses too many values of $\mathbf{z}$ to the same value of $\mathbf{x}$ to have enough diversity to model $p\_{\text {data }}$
  
  The sentence uses the name of a scenario called Helvetica. Is it related to AI or chemistry? If AI, then where can I read more about the scenario?
  
  **Tags:** terminology, papers, generative-adversarial-networks
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-05-18. Score: 1 | Accepted answer
  
  The "Helvetica scenario" referenced in the paper is an AI issue related to generative systems failing.
  
  In GANs, this might be due to a generator becoming initially strong against the discriminator by focusing on its best output results so far that cover just a subset of the full dataset - i.e. gradients move the generator towards output of a single best class, suppressing output of classes that the discriminator can spot easily, and encouraging output of classes where the discriminator has more trouble. By the time the discriminator catches up with the generator, it may be too late for the combined training system to learn its way out of this state.
  
  In GANs, the term *Helvetica scenario* from the paper is more commonly referred to as *mode collapse* in later literature. If you search for "GAN mode collapse" you will find a lot more information about it. There are a few different improvements to GAN design - loss functions and other hyperparameters - that work to reduce the incidence of mode collapse.
  
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  From *[this answer](https://datascience.stackexchange.com/questions/49744/where-can-i-find-out-about-the-helvetica-scenario)* on Data Science SE, it seems like the authors named the effect after a science parody sketch in the show Look Around You, where the structure of a molecule collapses. The "science" in the sketch is complete nonsense.
  
  
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