Invariance, Geometry and Deep Neural Networks with Pavan Turaga - #386
Published:Jun 25, 2020 17:08
•1 min read
•Practical AI
Analysis
This article summarizes a discussion with Pavan Turaga, an Associate Professor at Arizona State University, focusing on his research integrating physics-based principles into computer vision. The conversation likely revolved around his keynote presentation at the Differential Geometry in CV and ML Workshop, specifically his work on revisiting invariants using geometry and deep learning. The article also mentions the context of the term "invariant" and its relation to Hinton's Capsule Networks, suggesting a discussion on how to make deep learning models more robust to variations in input data. The focus is on the intersection of geometry, physics, and deep learning within the field of computer vision.
Key Takeaways
- •The article discusses Pavan Turaga's research on integrating physics-based principles into computer vision.
- •It highlights his work on revisiting invariants using geometry and deep learning.
- •The context of the term "invariant" and its relation to Hinton's Capsule Networks is also discussed.
Reference
“The article doesn't contain a direct quote, but it likely discussed the integration of physics-based principles into computer vision and the concept of "invariant" in relation to deep learning.”