This document provides an overview of generative adversarial networks (GANs). It begins with an introduction to supervised and unsupervised learning. It then discusses various generative models like autoencoders, variational autoencoders, and GANs. For GANs specifically, it explains that they involve two neural networks - a generator that produces new data instances, and a discriminator that evaluates them for authenticity. The generator and discriminator play a adversarial game, with the generator learning to produce more realistic samples to fool the discriminator while the discriminator gets better at identifying the generator's fakes. In the end, this competition helps the generator learn the true data distribution to a degree that it can produce highly realistic new samples.
14. داده؟ و یادگیری فضای!
Mohammad khalooeiGenerative Adversarial Network
ایداده فضای
Data space
نهان فضای
Latent space
https://goo.gl/XZVmCt
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37. Thank you!
Mohammad KHalooei
PhD student at Amirkabir University of Technology- Tehran Polytechnic
Laboratory of Intelligence and multimedia processing (limp.aut.ac.ir)
Big data work group at Sharif University of Technology (bigdataworkgroup.ir)
http://ceit.aut.ac.ir/~khalooei
khalooei@aut.ac.ir
Mohammad khalooeiGenerative Adversarial Network37
Dec 2017