MIT CSAIL - 10/27/2020

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 inside the deep representations learned from synthesizing images. In this talk, I will present our recent series work from GenForce ([ Ссылка ]) on interpreting and utilizing latent space of the GANs. Identifying these semantics not only allows us to better understand the internals of the generative models, but also facilitates versatile real image editing applications. Lastly, I will briefly talk about our recent effort of using generative modeling to improve the generalization of end-to-end autonomous driving.

Bio:
Bolei Zhou is an Assistant Professor with the Information Engineering Department at the Chinese University of Hong Kong. He received his PhD in computer science at the Massachusetts Institute of Technology. His research is on machine perception and decision making, 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. More about his research is at [ Ссылка ].

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