GENERATIVE ADVERSARIAL NETWORKS
Nejla Ghaboosi (IR)
Tuesday August 29 12:00pm
A substantial fraction of unsupervised learning research is driven by generative modelling. Generative Adversarial Networks (GANs) are a recently introduced class of generative models. This presentation tries to address: (1) why GANs are worth of studying, (2) how GANs compare to other generative models, (3) the details of how GANs work, and (4) practical tips and tricks that can be used to improve the performance of GANs.
Nejla Ghaboosi is Advanced Research Engineer at IR, where she conducts machine learning research in the performance management domain. She holds a PhD in Electrical Engineering from the University of Sydney and has a BSc and MSc in Computer Engineering. Her research interests involve deep learning and reinforcement learning.