This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). Generative Adversarial Networks Generative Adversarial Network framework. Generative Adversarial Network Some slides were adated/taken from various sources, including Andrew Ngâs Coursera Lectures, CS231n: Convolutional Neural Networks for Visual Recognition lectures, Stanford University CS Waterloo Canada lectures, Aykut Erdem, et.al. Generative Adversarial Networks (part 2) Benjamin Striner1 1Carnegie Mellon University April 22, 2020 Benjamin Striner CMU ... See recitations and tutorials for details Benjamin Striner CMU ... Adversarial optimization is a more general, harder problem than single-player optimization Rustem and Howe 2002) The GAN architecture is relatively straightforward, although one aspect that remains challenging for beginners is the topic of GAN loss functions. Ð..м. The main idea behind a GAN is to have two competing neural network models. Although Generative Adversarial Network (GAN) is an old idea arising from the game theory, they were introduced to the machine learning community in 2014 by Ian J. Goodfellow and co-authors in the article Generative Adversarial Nets.How does a GAN work and what is it good for? In a GAN setup, two differentiable functions, represented by neural networks, are locked in a game. Generative models Explicit density Implicit density Direct Tractable density Approximate density Markov Chain Variational Markov Chain Variational Autoencoder Boltzmann Machine GSN GAN Figure copyright and adapted from Ian Goodfellow, Tutorial on Generative Adversarial Networks, 2017. 654 p. The study of Generative Adversarial Networks GANs is new, just a few years old. The generative adversarial network, or GAN for short, is a deep learning architecture for training a generative model for image synthesis.. GANs are an interesting idea that were first introduced in 2014 by a group of researchers at the University of Montreal lead by Ian Goodfellow (now at OpenAI). : Jason Brownlee, 2019. (Goodfellow 2016) Adversarial Training ⢠A phrase whose usage is in ï¬ux; a new term that applies to both new and old ideas ⢠My current usage: âTraining a model in a worst-case scenario, with inputs chosen by an adversaryâ ⢠Examples: ⢠An agent playing against a copy of itself in a board game (Samuel, 1959) ⢠Robust optimization / robust control (e.g. Yet, in just a few years GANs have achieved results so remarkable that they have become the state-of-the-art in generative modeling. GANs are generative models devised by Goodfellow et al. One takes noise as input and generates samples (and so is called the generator). The two players (the generator and the discriminator) have different roles in this framework. UVA DEEP LEARNING COURSE âEFSTRATIOS GAVVES GENERATIVE ADVERSARIAL NETWORKS - 15 Implicit density models oNo explicit probability density function (pdf) needed oInstead, a sampling mechanism to draw samples from the pdf without knowing the pdf Generative Adversarial Networks. About: This is a NIPS 2016 video tutorial where Ian Goodfellow explained the basics of Generative adversarial networks (GANs). Today: discuss 3 most popular types of generative models today in 2014.