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Ioannis Pitas (AUTH)
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This lecture overviews Generative Adversarial Networks that have many applications in Media Production. It covers the following topics in detail: Theoretical ML background (cross-entropy loss for binary classification), Deep fake, Generator function, Discriminator function, GANs training using Minimax optimization or Heuristic optimization. The most notable GAN architectures are presented: cGAN, IcGAN, Convolutional GANs, LSTM-GAN, TP-GAN, Pix2Pix, CycleGAN, StarGAN, GauGAN, DeblurGAN, ID-CGAN, PerceptualGAN, 3D-GAN, MidiNet, StyleGAN, DiscoGAN, PG2.
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