Answers.Body: <p>Yes. You are right. The IS is bound by the number of classes.</p>
  <p>This paper titled &quot;<a href="https://arxiv.org/pdf/1801.01973.pdf" rel="nofollow noreferrer">A Note on the Inception Score</a>&quot; clearly shows a formal proof of the same. Please head to section 3.3 of the main text for a description and the appendix for the proof.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-13T19:03:18.640Z
Answers.LastActivityDate: 2021-08-13T19:03:18.640Z
Answers.UserDisplayName: ayandas
Body: <p>Inception score is used to evaluate the generative models. It is a score given based on quality and diversity of images generated.</p>
  <p>I have doubt about the range of inception score because of the reason that <a href="https://medium.com/octavian-ai/a-simple-explanation-of-the-inception-score-372dff6a8c7a" rel="nofollow noreferrer">an article</a> mentions about the possibility of range <span class="math-container">$[0, \infty]$</span> and still talks about upper bound in practical setting</p>
  <blockquote>
  <p>The lowest score possible is zero. Mathematically the highest possible
  score is infinity, although in practice there will probably emerge a
  non-infinite ceiling. For a ceiling to the IS, imagine that our
  generators produce perfectly uniform marginal label distributions and
  a single label delta distribution for each image — then the score
  would be bounded by the number of labels.</p>
  </blockquote>
  <p>Suppose I have 1000 classes/labels in my task, then is it possible to get an inception score of 2000? Or is it mandatory that the inception score <a href="https://machinelearningmastery.com/how-to-implement-the-inception-score-from-scratch-for-evaluating-generated-images/" rel="nofollow noreferrer">must lie</a> in <span class="math-container">$[1, 1000]$</span>?</p>
  <p>To be concise: Is bounding inception score to a particular range <span class="math-container">$[1, \text{number of classes}]$</span> optional or mandatory?</p>
  
Tags: <math><generative-model><image-generation>
Title: Is the range of inception score flexible or bounded based on number of classes?
UserDisplayName: hanugm
ThreadText: # Is the range of inception score flexible or bounded based on number of classes?
  
  (Asked by: username_0 on 2021-08-08)
  
  Inception score is used to evaluate the generative models. It is a score given based on quality and diversity of images generated.
  
  I have doubt about the range of inception score because of the reason that [an article](https://medium.com/octavian-ai/a-simple-explanation-of-the-inception-score-372dff6a8c7a) mentions about the possibility of range $[0, \infty]$ and still talks about upper bound in practical setting
  
  > The lowest score possible is zero. Mathematically the highest possible
  > score is infinity, although in practice there will probably emerge a
  > non-infinite ceiling. For a ceiling to the IS, imagine that our
  > generators produce perfectly uniform marginal label distributions and
  > a single label delta distribution for each image — then the score
  > would be bounded by the number of labels.
  
  Suppose I have 1000 classes/labels in my task, then is it possible to get an inception score of 2000? Or is it mandatory that the inception score [must lie](https://machinelearningmastery.com/how-to-implement-the-inception-score-from-scratch-for-evaluating-generated-images/) in $[1, 1000]$?
  
  To be concise: Is bounding inception score to a particular range $[1, \text{number of classes}]$ optional or mandatory?
  
  **Tags:** math, generative-model, image-generation
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-08-13. Score: 1
  
  Yes. You are right. The IS is bound by the number of classes.
  
  This paper titled "[A Note on the Inception Score](https://arxiv.org/pdf/1801.01973.pdf)" clearly shows a formal proof of the same. Please head to section 3.3 of the main text for a description and the appendix for the proof.
  
  
  ---
  
  