Answers.Body: <p>The contradictory loss is the same loss function that the discriminator would normally use, except with deliberately incorrect labels. That is, when you train the generator, the output of the generator is fed to the discriminator, but instead of the correct label (typically <span class="math-container">$0$</span> for a false image), the opposite label is applied (e.g. <span class="math-container">$1$</span> for a real image).</p>
  <p>This label is condractictory in that it is incorrect and the opposite of the ground truth. However, this is useful, because reducing the loss of this signal represents the goal of the generator. It is important to note that during this phase of the training, you should <em>not</em> update any parameters of the discriminator. The process is followed in order to find gradients for improving the generator only.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-12-07T18:07:55.643Z
Answers.LastActivityDate: 2020-12-07T18:07:55.643Z
Answers.UserDisplayName: Neil Slater
Body: <p>In page 4 of the paper <a href="https://arxiv.org/pdf/2009.07047v1.pdf" rel="nofollow noreferrer">Old Photo Restoration via Deep Latent Space
  Translation</a>, it says the encoder <span class="math-container">$E_{R,X}$</span> of <span class="math-container">$VAE_1$</span> tries to fool the discriminator with a contradictory loss to ensure that <span class="math-container">$R$</span> and <span class="math-container">$X$</span> are mapped to the same space. What do they mean by &quot;contradictory loss&quot;?</p>
  
Tags: <terminology><papers><generative-adversarial-networks><variational-autoencoder><loss>
Title: What is the "contradictory loss" in the "Old Photo Restoration via Deep Latent Space Translation" paper?
UserDisplayName: Robert
ThreadText: # What is the "contradictory loss" in the "Old Photo Restoration via Deep Latent Space Translation" paper?
  
  (Asked by: username_0 on 2020-12-07)
  
  In page 4 of the paper [Old Photo Restoration via Deep Latent Space
  Translation](https://arxiv.org/pdf/2009.07047v1.pdf), it says the encoder $E\_{R,X}$ of $VAE\_1$ tries to fool the discriminator with a contradictory loss to ensure that $R$ and $X$ are mapped to the same space. What do they mean by "contradictory loss"?
  
  **Tags:** terminology, papers, generative-adversarial-networks, variational-autoencoder, loss
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-12-07. Score: 1
  
  The contradictory loss is the same loss function that the discriminator would normally use, except with deliberately incorrect labels. That is, when you train the generator, the output of the generator is fed to the discriminator, but instead of the correct label (typically $0$ for a false image), the opposite label is applied (e.g. $1$ for a real image).
  
  This label is condractictory in that it is incorrect and the opposite of the ground truth. However, this is useful, because reducing the loss of this signal represents the goal of the generator. It is important to note that during this phase of the training, you should *not* update any parameters of the discriminator. The process is followed in order to find gradients for improving the generator only.
  
  
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