Difference between revisions of "Generative Adversarial Network (GAN)"
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| − | [ | + | [http://www.youtube.com/results?search_query=gan+adversarial+network+ YouTube search...] |
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| + | * [https://deeplearning4j.org/generative-adversarial-network.html Guide] | ||
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| + | Comprised of two nets, pitting one against the other (thus the “adversarial”). GANs’ potential is huge, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds eerily similar to our own in any domain: images, music, speech, prose. Discriminative algorithms map features to labels. They are concerned solely with that correlation. One way to think about generative algorithms is that they do the opposite. Instead of predicting a label given certain features, they attempt to predict features given a certain label. | ||
<youtube>Sw9r8CL98N0</youtube> | <youtube>Sw9r8CL98N0</youtube> | ||
Revision as of 22:21, 10 May 2018
Comprised of two nets, pitting one against the other (thus the “adversarial”). GANs’ potential is huge, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds eerily similar to our own in any domain: images, music, speech, prose. Discriminative algorithms map features to labels. They are concerned solely with that correlation. One way to think about generative algorithms is that they do the opposite. Instead of predicting a label given certain features, they attempt to predict features given a certain label.
