Lecture 10 & 11 Flashcards
What are steerable filters? What are they most useful for?
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What is Cortexica?
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What is the basis from “Where’s Wally?” What areas of the brain are involved?
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How does the John Lewis app work? What does it use?
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What are the different components of the macroscopic view of the visual system?
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What is RGC? What is LGN?
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What is the function of the LGN?
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What is the function of V1? What makes it so special?
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What is the retinal structure like microscopically? What is so special? What does this mean for the function?
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What are the different layers? What are their properties generally?
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What is the structure of the cortex?
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How to describe neuron behaviour?
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What is STA?
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What is reverse correlation?
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What is a receptive field?
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What is the response of a single cell to the orientation to the bar? What was this experiment?
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What is the map of multiple cells? How is it organised? What is an important property?
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What are the differences in the different layers of the visual cortex (V1)?
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What is the different in circular variance in V1?
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What are the different types of cells? How are they classified? How are they sorted in the layers?
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What are the repeating pattern for layers of neurons?
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What are the repeating pattern for single neurons?
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What kind of patterns are needed to build an algorithm that represents the biological visual system?
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How do the cells of V2 and V4 respond to different structures?
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What were the different machine learning over the years?
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What were the properties of the Granlund?
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What were the properties of the Neocognitron?
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What were the properties of the LeNet?
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What were the properties of the AlexNet?
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What is convolution? What does it do?
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What is the linear component?
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What is the non-linear component?
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What is the activation function?
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What is the bias?
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What did AlexNet do? What can it do?
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What was the planned architecture?
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What changed in it to make it a more realistic architecture?
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What is the multilayer convolution system? How does it work? What are the stages?
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How is training done in a multilayer convolution system?
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How does AlexNet work? What ways is it similar?
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What are the receptive fields in the artificial networks at different levels? How do they compare to biological networks?
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What is the computational heavy part? What is a consequence of this?
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What was the solution to this which was used in Cortexica?
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How to synthesise the effective response to different types of spatial phase without using significant amounts of computation? What is the limitation?
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How is the fingerprint like structure used in the John Lewis app?
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How can you synthesise the effective response to all different angles?
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What is the general summary of Steerable filters?
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