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Image Classification Variations

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Questions and Answers

What is the main challenge in manually extracting features from image data?

Lack of suitable software tools

Incredible variability in image data (correct)

Too much sensitivity to occlusions

Difficulty in detecting brightness values

Why is it important for a classification pipeline to be invariant to certain variations?

To ensure consistent classification regardless of certain image variations (correct)

To focus only on occlusions

To make it more sensitive to intra-class variations

To ignore all variations within classes

How can neural networks help in feature extraction from images?

By avoiding hierarchical feature learning

By introducing more occlusions

By learning visual features directly from data (correct)

By manually defining features

What is the benefit of learning a hierarchy of features in image analysis?

To make features less brittle Signup and view all the answers

Which approach is suggested for reconstructing a representation of the final class label?

Learning visual features directly from data Signup and view all the answers

What do neural networks aim to achieve in feature extraction from images?

Learn a hierarchy of features from data Signup and view all the answers

What type of neural networks were discussed in lecture one?

Fully connected Signup and view all the answers

What happens when a two-dimensional image is fed into a fully connected network for image classification?

The spatial structure of the image is lost Signup and view all the answers

Why is it mentioned that densely connected networks lose the spatial structure of images?

To emphasize the importance of spatial structure Signup and view all the answers

What is a drawback of using densely connected neural networks for image processing?

Loss of spatial structure Signup and view all the answers

In densely connected networks, what does it mean that every input is connected to every output in a layer?

High connectivity within the network Signup and view all the answers

How does collapsing a two-dimensional image into a one-dimensional vector affect the subsequent processing?

Spatial information is lost Signup and view all the answers