Answers.Body: <p>GANs are usually trained in a <a href="https://ai.stackexchange.com/a/10624/2444"><strong>self-supervised</strong> fashion</a>, i.e. they use the unlabelled data as the supervisory signal. Note that some self-supervised learning methods are unsupervised learning techniques, given that no human-annotated data is needed. However, not all SSL techniques are used for solving an unsupervised learning task. In fact, there are SSL techniques that are specifically used to generate labeled data, which can then be used to train a model in a supervised fashion.</p>
  
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Answers.CreationDate: 2020-08-02T00:10:48.800Z
Answers.LastActivityDate: 2020-08-02T00:10:48.800Z
Answers.UserDisplayName: nbro
Answers.Body: <p>The terms <em>Supervised Learning</em> and <em>Unsupervised Learning</em> predate the invention of the application of artificial networks to a generative and discriminative network pair, which was the first popular generative topology.  The existence of labeling is the key distinction between the two.  Even partial labeling indicates supervision, as odd as that jargon is, since the supervisor does no learning and the labels are constants.</p>
  
  <p>Based on the original description of the discriminative network in a GAN, that it consumes examples without labels, GANs are a type of unsupervised learning.  That fact does not eliminate the use of labels as part of an extension of the original design that generates based on some labeled element in the examples or the use of other labels to indicate the fitness of each image for a class into which generated images are expected to fall.</p>
  
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Answers.CreationDate: 2018-11-28T23:36:13.520Z
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Body: <p>Do GANs come under supervised learning or unsupervised learning?</p>
  
  <p>My guess is that they come under supervised learning, as we have labeled dataset of images, but I am not sure as there might be other aspects in GANs which might come into play in the determination of the class of algorithms GAN falls under.</p>
  
Tags: <neural-networks><terminology><unsupervised-learning><generative-adversarial-networks><supervised-learning>
Title: Do GANs come under supervised learning or unsupervised learning?
UserDisplayName: codecracker
ThreadText: # Do GANs come under supervised learning or unsupervised learning?
  
  (Asked by: username_0 on 2018-11-10)
  
  Do GANs come under supervised learning or unsupervised learning?
  
  My guess is that they come under supervised learning, as we have labeled dataset of images, but I am not sure as there might be other aspects in GANs which might come into play in the determination of the class of algorithms GAN falls under.
  
  **Tags:** neural-networks, terminology, unsupervised-learning, generative-adversarial-networks, supervised-learning
  
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  ## 2 answers
  
  ### Answer by username_1 on 2020-08-02. Score: 1
  
  GANs are usually trained in a [**self-supervised** fashion](https://ai.stackexchange.com/a/10624/2444), i.e. they use the unlabelled data as the supervisory signal. Note that some self-supervised learning methods are unsupervised learning techniques, given that no human-annotated data is needed. However, not all SSL techniques are used for solving an unsupervised learning task. In fact, there are SSL techniques that are specifically used to generate labeled data, which can then be used to train a model in a supervised fashion.
  
  
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  ### Answer by username_2 on 2018-11-28. Score: 1
  
  The terms *Supervised Learning* and *Unsupervised Learning* predate the invention of the application of artificial networks to a generative and discriminative network pair, which was the first popular generative topology. The existence of labeling is the key distinction between the two. Even partial labeling indicates supervision, as odd as that jargon is, since the supervisor does no learning and the labels are constants.
  
  Based on the original description of the discriminative network in a GAN, that it consumes examples without labels, GANs are a type of unsupervised learning. That fact does not eliminate the use of labels as part of an extension of the original design that generates based on some labeled element in the examples or the use of other labels to indicate the fitness of each image for a class into which generated images are expected to fall.
  
  
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