Topic: Generative Adversarial Networks
Date: February 27th 2017 at 4:00pm
Location: Rm 4421, CS Thesis Room
Presenter: Allan Zelener
Links: Paper, Videos
Generative Adversarial Networks [Goodfellow et al., 2014] is a recent seminal paper combining generative models, adversarial training, and neural networks. There has been much interest in GANs over the past few years due to their potential for learning with limited training data and ground truth labels compared to traditional discriminative neural networks. Many open research issues exist for GANs such as stabilizing the training procedure. For the first meeting of the ML Student Seminar in 2017 we will be viewing and discussing videos from the NIPS 2016 Workshop on Adversarial Training including Introduction to GANs by Ian Goodfellow.
New Format: This year the MLSS (formerly MLRG) will adopt a new format where each member is assigned a date to lead the discussion and may choose to reschedule or cancel the meeting on that date if they are unavailable. The goal of this format is to prevent the need for finding volunteers week to week. It is also encouraged for discussion leaders to select videos from conferences as an alternative to preparing a full presentation themselves if the meeting would otherwise be canceled.
