Title: Splitting Algorithms for Training GANs

Speaker: Matthew Tam (University of Melbourne)

Date: Thu, 14/05/2020 – 11:05am

Venue: Zoom meeting (connection details here)

Abstract: Generative adversarial networks (GANs) are an approach to fitting generative models over complex structured spaces. Within this framework, the fitting problem is posed as a zero-sum game between two competing neural networks which are trained simultaneously. Mathematically, this problem takes the form of a saddle-point problem; a well-known example of the type of problem where the usual (stochastic) gradient descent-type approaches used for training neural networks fail. In this talk, we rectify this shortcoming by proposing a new method for training GANs that has both: (i) theoretical guarantees of convergence, and (ii) does not increase the algorithm’s per iteration complexity (as compared to gradient descent). The theoretical analysis is performed within the framework of monotone operator splitting.