Lecture 9

Question 1

What is the simplified anatomy of the human visual cortex? What are the main pathways?

Answer 1

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Question 2

Why is natural pattern recognition a hard problem for robots?

Answer 2

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Question 3

What is the example experiment showing selectivity and invariance?

Answer 3

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Question 4

How is selectivity achieved?

Answer 4

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Question 5

How is invariance achieved?

Answer 5

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Question 6

What are the operations for selectivity and invariance in the visual cortex?

Answer 6

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Question 7

What is Gaussian tuning?

Answer 7

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Question 8

What are canonical circuits? How do they work? What are the components?

Answer 8

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Question 9

What is the equations for the output of a canonical circuit?

Answer 9

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Question 10

What are the different types of functions that can come out of a canonical circuit? How is this possible?

Answer 10

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Question 11

What is the hierarchy system of the HMAX model? How does it compare to humans?

Answer 11

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Question 12

How did google implement this? Using what?

Answer 12

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Question 13

How does the learning happen? How does the system do this? What does this mean for DNN’s?

Answer 13

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Question 14

How are DNNs easily fooled?

Answer 14

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Question 15

What can applying imperceptibles non-random perturbations (small changes) to test images do in DNNs? What do these results mean? What is the consequence of this?

Answer 15

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