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"Learning to See" Public talk by Professor Antonio Torralba (MIT-CSAIL)

Date / Time:
25/03/2019 Download ICS File
Category:
Events

Visit by Antonio Torralba, who teaches machines to automate tasks that a human visual system can accomplish, is part of annual spring research update between QCRI and MIT-CSAIL.

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Title: Learning to See

It is an exciting time for computer vision. With the success of new computational architectures for visual processing, such as deep neural networks (e.g., ConvNets) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. Even when no examples are available, Generative Adversarial Networks (GANs) have demonstrated a remarkable ability to learn from images and are able to create nearly photorealistic images. The performance achieved by ConvNets and GANs is remarkable and constitute the state of the art on many tasks. But why do ConvNets work so well? What is the nature of the internal representation learned by a ConvNet in a classification task? How does a GAN represent our visual world internally? In this talk Prof. Torralba will show that the internal representation in both ConvNets and GANs can be interpretable in some important cases. Prof. Torralba will then show several applications for object recognition, computer graphics, and unsupervised learning from images and audio.  

About Antonio Torralba

Dr. Antonio Torralba is a professor of electrical engineering and computer science at MIT and a principal investigator at the Computer Science and Artificial Intelligence Laboratory. He is also the MIT director of the MIT–IBM Watson AI Lab and the inaugural director of The MIT Quest for Intelligence. Dr. Torralba researches computer vision, machine learning, and human visual perception, with an interest in building systems that can perceive the world the way humans do. He was honored with a National Science Foundation CAREER award, the J.K. Aggarwal Prize from the International Association for Pattern Recognition, the Frank Quick Faculty Research Innovation Fellowship, and the Louis D. Smullin (’39) Award for Teaching Excellence. Torralba earned a BS from Telecom BCN, Spain, and a PhD from the Institut National Polytechnique de Grenoble, France. He did his postdoctoral work at MIT. Dr. Torralba’s current research includes a multi-year collaboration with QCRI’s Dr. Ferda Ofli and Dr. Ingmar Weber. Their work is focused on using machine learning to analyze social media images, in order to better understand food habits in Qatar.

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