ML@GT Virtual Seminar: Bolei Zhou, The Chinese University of Hong Kong

Interpretable latent space and inverse problem in deep generative models

Abstract:

Recent progress in deep generative models such as Generative Adversarial Networks (GANs) has enabled synthesizing photo-realistic images, such as faces and scenes. However, it remains much less explored on what has been learned in the deep generative representation and why diverse realistic images can be synthesized. In this talk, I will present our recent series work from GenForce (https://genforce.github.io/) on interpreting and utilizing latent space of the GANs. Identifying these semantics not only allows us to better understand the inner working of the deep generative models but also facilitates versatile image editings. I will also briefly talk about the inverse problem (how to invert a given image into the latent code) and the fairness of the generative model.

Bio

Bolei Zhou is an Assistant Professor with the Information Engineering Department at the Chinese University of Hong Kong. He earned his PhD in computer science at the Massachusetts Institute of Technology. His research is on machine perception and autonomy, with a focus on enabling interpretable human-AI interactions. He received the MIT Tech Review’s Innovators under 35 in Asia-Pacific Award, Facebook Fellowship, Microsoft Research Asia Fellowship, MIT Greater China Fellowship, and his research was featured in media outlets such as TechCrunch, Quartz, and MIT News.