Answers.Body: <p>if you fix the seed (all random factors) you should get similar results.
  but if you only fix the shuffle and noise, it might be not enough. e.g if you train a GAN, then starting point (initialization) of the GAN is not the same always, so the trained GAN will be different at the end.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-07-09T13:27:05.647Z
Answers.LastActivityDate: 2024-07-09T13:27:05.647Z
Answers.UserDisplayName: saleh
Body: <p>If the dataset, shuffle, and noise are all kept the same, is it possible for the same GAN to give different Inception Scores each time?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-07-05T12:45:26.117Z
Comments.Text: Welcome to AI stackexchange. If all parts of your application are deterministic, then your inception score should, given the same input, result in the same score. However, there might be ways in which your application is indeterministic, resulting in different scores each time.
Comments.UserDisplayName: Robin van Hoorn
Tags: <generative-adversarial-networks><inception><dc-gan><noise>
Title: Can a GAN Produce Different Inception Scores with the Same Dataset and Noise?
UserDisplayName: odbhut.shei.chhele
ThreadText: # Can a GAN Produce Different Inception Scores with the Same Dataset and Noise?
  
  (Asked by: username_0 on 2024-07-04)
  
  If the dataset, shuffle, and noise are all kept the same, is it possible for the same GAN to give different Inception Scores each time?
  
  **Tags:** generative-adversarial-networks, inception, dc-gan, noise
  > **Comments:**
  > **username_1**: Welcome to AI stackexchange. If all parts of your application are deterministic, then your inception score should, given the same input, result in the same score. However, there might be ways in which your application is indeterministic, resulting in different scores each time.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2024-07-09. Score: 1
  
  if you fix the seed (all random factors) you should get similar results.
  but if you only fix the shuffle and noise, it might be not enough. e.g if you train a GAN, then starting point (initialization) of the GAN is not the same always, so the trained GAN will be different at the end.
  
  
  ---
  
  