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1
Intro
2
Universal Approximator
3
Face Recognition
4
Image Noise
5
Tesla Autopilot
6
Clever
7
Same Different
8
Horizontal and stopdown connections
9
Extraclassical receptive fields
10
Computational neuroscience
11
Breaking constraint
12
Contextual illusions
13
Tilt illusion
14
Benefits of recurrent connections
Description:
Explore the computational foundations of primate and machine vision in this lecture by Thomas Serre from Brown University. Delve into topics such as universal approximators, face recognition, image noise, and Tesla's Autopilot system. Examine the 'Same-Different' problem and investigate horizontal and top-down connections in visual processing. Gain insights into extraclassical receptive fields and their role in computational neuroscience. Analyze how breaking constraints and contextual illusions, including the tilt illusion, impact visual perception. Discover the benefits of recurrent connections in visual systems and their implications for both biological and artificial vision.

What Are the Computations Underlying Primate vs. Machine Vision?

MITCBMM
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