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Spring 2018 GRASP Seminar Series: Kate Saenko, Boston University, “Adversarial Adaptation for Object Recognition and Manipulation”

March 16, 2018 @ 11:00 am - 12:00 pm


Real-world robotics problems often occur in domains that differ significantly from the robot’s prior training environment. For many robotic perception tasks, real world experience is expensive to obtain, but data is easy to collect in either an instrumented environment or in simulation.  However, perception models trained on such data often do not generalize to real-world environments. I will describe several recent approaches that we have developed to address this so-called domain shift problem. In particular, I will show that adversarial learning techniques can adapt visual representations learned on large easy-to-obtain source datasets (e.g. synthetic images) to a target real-world domain, without requiring expensive manual data annotation of real world data.


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Kate Saenko is an Associate Professor of Computer Science at Boston University, director of the Computer Vision and Learning Group and co-director of the AI Research initiative at BU. Her past academic positions include: Assistant Professor at the Computer Science Department at UMass Lowell, Postdoctoral Researcher at ICSI, Visiting Scholar at UC Berkeley EECS and a Visiting Postdoctoral Fellow in the School of Engineering and Applied Science at Harvard University. Her research interests are in the broad area of Artificial Intelligence with a focus on Adaptive Machine Learning, Learning for Vision and Language Understanding, and Deep Learning.


March 16, 2018
11:00 am - 12:00 pm
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