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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.

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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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GANs are usually trained in a self-supervised fashion, 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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GANs are unsupervised machine learning algorithms. According to Wikipedia, unsupervised algorithms are :

Unsupervised learning is a branch of machine learning that learns from test data that has not been labelled, classified or categorized

In GAN networks, only training data is provided which is not labelled. The network generates candidates ( generative ) which are evaluated by the discriminator. The network slowly learns from the data given from a latent space.

Suppose, you want to create a GAN network which can make a Monet painting. You just need to feed it some Monet paintings. Here, you are not interested in classifying the painting, but copying/mimicking it.

Hence, there is no need for labels here which makes GAN an unsupervised machine learning algorithm.

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