Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks | Summary and Q&A

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April 3, 2018
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Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks

TL;DR

i-Theory explores the concept of feedforward processing in the visual cortex and its role in deep learning networks.

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Key Insights

  • ❓ The visual cortex is believed to utilize feedforward processing, with most processing occurring in the first 100 milliseconds of visual perception.
  • 👋 Feedforward models, such as deep learning networks, can achieve surprisingly good results in object recognition despite their simplicity.
  • 🏷️ Invariance to transformations, such as rotation and translation, can greatly improve recognition performance and require fewer labeled examples for training.

Transcript

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Questions & Answers

Q: How does i-Theory propose to compute an invariant representation in the visual cortex?

i-Theory suggests using templates and dot products to compute feature vectors that are invariant to transformations such as rotation and translation. By pooling the dot product values, histograms or moments can be used to represent the invariant features.

Q: Why is invariance important in object recognition?

Invariance allows for recognition of objects despite variations in scale, position, and orientation. It makes recognition easier by reducing the number of labeled examples needed for training.

Q: What is the relationship between simple and complex cells in the visual cortex?

Simple and complex cells may be the same cell performing different operations. Simple cells compute dot products with receptive fields, while complex cells pool the outputs of simple cells. Nonlinearities and dendritic properties play a role in these computations.

Q: How does the hierarchy of modules in the visual cortex contribute to invariance and selectivity?

The hierarchy of modules increases spatial pooling and scale pooling as the signal moves up the hierarchy. This allows for both invariance to transformations and selectivity to different objects through pooling and template matching.

Summary & Key Takeaways

  • i-Theory proposes that the visual cortex aims to compute a set of features that are invariant to transformations observed during development while remaining selective.

  • The theory suggests using templates and dot products to compute feature vectors that are invariant to various transformations such as rotation and translation.

  • A hierarchy of modules, similar to deep learning networks, can be used to achieve invariance and selectivity in object recognition.

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