Invariance, Geometry and Deep Neural Networks with Pavan Turaga

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We continue our CVPR coverage with today’s guest, Pavan Turaga, Associate Professor at Arizona State University, with dual appointments as the Director of the Geometric Media Lab, and Interim Director of the School of Arts, Media, and Engineering.

Pavan gave a keynote presentation at the Differential Geometry in CV and ML Workshop, speaking on Revisiting Invariants with Geometry and Deep Learning. In our conversation, we go in-depth on Pavan’s research integrating physics-based principles into computer vision. We also discuss the context of the term “invariant,” and the role of architectural, loss function, and data constraints on models. Pavan also contextualizes this work in relation to Hinton’s similar Capsule Network research.

Thanks to our sponsor

I’d like to send a huge thank you to our friends at Qualcomm for their support of the podcast, and their sponsorship of this series! Qualcomm AI Research is dedicated to advancing AI to make its core capabilities — perception, reasoning, and action — ubiquitous across devices. Their work makes it possible for billions of users around the world to have AI-enhanced experiences on Qualcomm Technologies-powered devices. To learn more about what Qualcomm is up to on the research front, visit here.

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“More On That Later” by Lee Rosevere licensed under CC By 4.0

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