GANs are relativity new in the machine learning world, but they have proven to be a very powerful model. Recently, they made headlines in the DeepFake network, being able to mimic someone else in real time video and audio. There has also been cycleGAN, which takes one domain (horses) and makes it look like something similar (zebras). Come and learn the secret behind these type of networks, you will be suprised how intuitive it is! The lecture will cover the basics of GANs and different types, with the workshop covering how we can generate human faces, cats, dogs, and other cute creatures!
GANs are relativity new in the machine learning world, but they have proven to be a very powerful architecture. Recently, they made headlines in the DeepFake network, being able to mimic someone else in real time in both video and audio. There has also been cycleGAN, which takes one domain (horses) and makes it look like something similar (zebras). Come and learn the secret behind these type of networks, you will be surprised how intuitive it is! The lecture will cover the basics of GANs and different types, with the workshop covering how we can generate human faces, cats, dogs, and other cute creatures!
Abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through