In these lectures, at long last, we will discuss Generative Adversarial Networks (GANs). GANs are a recent and very popular generative model paradigm. We will discuss the GAN formalism, some theory and practical considerations.
Reference: (* = you are responsible for this material)
- *Sections 20.10.4 of the Deep Learning textbook.
- *NIPS 2016 Tutorial: Generative Adversarial Networks by Ian Goodfellow, arXiv:1701.00160v1, 2016
- *Generative Adversarial Networks by Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio (NIPS 2014).
- *f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization by Sebastian Nowozin, Botond Cseke and Ryota Tomioka (NIPS 2016).
- *Adversarially Learned Inference by Vincent Dumoulin , Ishmael Belghazi , Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky and Aaron Courville (ICLR 2017).
- Other reference are provided in Ian Goodfellow’s slides.