Body: <p>Let's say we have a WGAN where the generator and critic have 8 layers and 5 million parameters each. I know that the greater the number of training samples the better, but is there a way to know the minimum number of training examples needed? Does it depend on the size of the network or the distribution of the training set? How can I estimate it?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-24T15:29:28.333Z
Comments.Text: Hello. To clarify, are you only interested in WGAN? In any case, it seemed to me that was the case, so I tried to clarify that in your post. So, are you also just interested in the task of learning a probability distribution to generate samples? In other words, which machine learning task are you interested in? It may also be a good idea to be more specific when you say "interesting results", although, in this case, this detail should be obvious (although dependent on the task and performance measure).
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-24T19:06:28.033Z
Comments.Text: Sorry, I just started with machine learning less that a month ago. With interesting I mean like what WDCGANs usually do, generate elements that look similar to the samples without being the same. I made the quiestion thinking about GANs but if someone gives guidelines for any ANN, that would be good.
Comments.UserDisplayName: FalseSemiColon
Tags: <computational-learning-theory><wasserstein-gan><sample-efficiency>
Title: How can I estimate the minimum number of training samples needed to get interesting results with WGAN?
UserDisplayName: FalseSemiColon
ThreadText: # How can I estimate the minimum number of training samples needed to get interesting results with WGAN?
  
  (Asked by: username_0 on 2021-02-24)
  
  Let's say we have a WGAN where the generator and critic have 8 layers and 5 million parameters each. I know that the greater the number of training samples the better, but is there a way to know the minimum number of training examples needed? Does it depend on the size of the network or the distribution of the training set? How can I estimate it?
  
  **Tags:** computational-learning-theory, wasserstein-gan, sample-efficiency
  
  > **Comments:**
  > **username_1**: Hello. To clarify, are you only interested in WGAN? In any case, it seemed to me that was the case, so I tried to clarify that in your post. So, are you also just interested in the task of learning a probability distribution to generate samples? In other words, which machine learning task are you interested in? It may also be a good idea to be more specific when you say "interesting results", although, in this case, this detail should be obvious (although dependent on the task and performance measure).  
  > **username_0**: Sorry, I just started with machine learning less that a month ago. With interesting I mean like what WDCGANs usually do, generate elements that look similar to the samples without being the same. I made the quiestion thinking about GANs but if someone gives guidelines for any ANN, that would be good.  
  
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  ## 0 answers
  
  