Iterative Approach To Improving Sample Generation
Generative modelling allows us to synthesise new data samples, whether the samples be used for imagining new concepts, augmenting datasets or better understanding our datasets. When synthesising samples, generative models don’t always get it right first time - the samples may not be sharp, may have artefacts or may be nonsensical. I will present recent research that shows how we can improve these samples, by applying an iterative procedure to get slightly better samples with each step.
Antonia is a PhD candidate at Imperial College London, in the Bio-Inspired Computer Vision Group. Her research focuses on unsupervised learning and generative models. She received her masters in Biomedical Engineering from Imperial College London with an exchange year at the University of California, Davis. Antonia has interned at DeepMind, Twitter (Magic Pony), Cortexica and UNMADE.