<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>

CC BY-SA 4.0
2021-02-24T15:29:28.333Z
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).
nbro
CC BY-SA 4.0
2021-02-24T19:06:28.033Z
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.
FalseSemiColon
<computational-learning-theory><wasserstein-gan><sample-efficiency>
How can I estimate the minimum number of training samples needed to get interesting results with WGAN?
FalseSemiColon
# 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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