Body: <p>I have a Conditional Generative Adversarial Network for Quantum State Tomography. The metrics I am monitoring during the training process are the losses and the Fidelity (the degree of similarity between two matrices). Fidelity close to <strong>1</strong> is good and close to <strong>0</strong> is bad. My result is a Fidelity over 0.999 which is excellent but the losses of both Discriminator and Generator are opposite to what I would expect.</p>
  <p>Fidelity is continuously rising which means that the matrices being generated by the network are becoming closer and closer to my target:</p>
  <p><a href="https://i.sstatic.net/VP1H0.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/VP1H0.png" alt="enter image description here" /></a></p>
  <p>But the losses:</p>
  <p><a href="https://i.sstatic.net/wkaei.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/wkaei.png" alt="enter image description here" /></a></p>
  <p>Why is that? Is something wrong or is this acceptable?</p>
  <p>What I would expect is the loss of the Discriminator going up - it is becoming harder and harder to distinguish between real and fake data - and the loss of Generator going down. But the opposite happened. I would say &quot;Well, my cGAN is no good and I need to rework it&quot; but the result is good! The Generator is able to generate a matrix that closely resembles the target even though it is becoming worse during training.</p>
  <p>Am I not getting something?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-11-27T20:59:12.230Z
Comments.Text: I've never seen a GAN reach equilibrium, indeed usually the stopping criterion is an empirical evaluation of the generator... it's much easier to discriminate to generate, so it's difficult for a generator to keep the pace
Comments.UserDisplayName: Alberto
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-11-28T13:59:27.997Z
Comments.Text: but is this result ok? I mean, I wasn't expecting equilibrium, but I believe that the Generator must have a lower value of its cost function than the Discriminator. The Generator's loss is too high and the disc loss is reaching zero, but the Generator are generating a matrix almost identical to the target. Is this ok? Am I not understanding something about GANs? hehe
Comments.UserDisplayName: Dimitri
Tags: <tensorflow><python><generative-adversarial-networks>
Title: cGAN: Discriminator loss going to zero while Generator's going always up but the result is very good
UserDisplayName: Dimitri
ThreadText: # cGAN: Discriminator loss going to zero while Generator's going always up but the result is very good
  
  (Asked by: username_0 on 2023-11-27)
  
  I have a Conditional Generative Adversarial Network for Quantum State Tomography. The metrics I am monitoring during the training process are the losses and the Fidelity (the degree of similarity between two matrices). Fidelity close to **1** is good and close to **0** is bad. My result is a Fidelity over 0.999 which is excellent but the losses of both Discriminator and Generator are opposite to what I would expect.
  
  Fidelity is continuously rising which means that the matrices being generated by the network are becoming closer and closer to my target:
  
  [![enter image description here](https://i.sstatic.net/VP1H0.png)](https://i.sstatic.net/VP1H0.png)
  
  But the losses:
  
  [![enter image description here](https://i.sstatic.net/wkaei.png)](https://i.sstatic.net/wkaei.png)
  
  Why is that? Is something wrong or is this acceptable?
  
  What I would expect is the loss of the Discriminator going up - it is becoming harder and harder to distinguish between real and fake data - and the loss of Generator going down. But the opposite happened. I would say "Well, my cGAN is no good and I need to rework it" but the result is good! The Generator is able to generate a matrix that closely resembles the target even though it is becoming worse during training.
  
  Am I not getting something?
  
  **Tags:** tensorflow, python, generative-adversarial-networks
  > **Comments:**
  > **username_1**: I've never seen a GAN reach equilibrium, indeed usually the stopping criterion is an empirical evaluation of the generator... it's much easier to discriminate to generate, so it's difficult for a generator to keep the pace  
  > **username_0**: but is this result ok? I mean, I wasn't expecting equilibrium, but I believe that the Generator must have a lower value of its cost function than the Discriminator. The Generator's loss is too high and the disc loss is reaching zero, but the Generator are generating a matrix almost identical to the target. Is this ok? Am I not understanding something about GANs? hehe  
  
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  ## 0 answers
  
  