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Image Classification Pipeline Variations

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Image Classification Pipeline Variations

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Questions and Answers

What are images in the context of the text?

Bright pixels arranged randomly

One-dimensional arrays of brightness values

Three-dimensional arrays of brightness values (correct)

Two-dimensional arrays of brightness values

Why is manual extraction of features from images considered difficult?

Due to the fixed nature of features in images

Because the features are easy to detect

The variability in image data makes detection challenging (correct)

The features are always clearly labeled in images

What is the key aspect when building a classification pipeline according to the text?

Being sensitive to variations within a single class

Not worrying about variations at all

Being invariant to variations within a single class (correct)

Ignoring variations between classes

How are neural networks proposed to help in image analysis?

By learning visual features directly from data Signup and view all the answers

What is the suggested approach for detecting visual features in images?

Learn a hierarchy of features and reconstruct class labels directly Signup and view all the answers

Why is it important to be sensitive to inter-class variations in classification?

To ensure accurate classification across different classes Signup and view all the answers

What type of neural network did we learn about in lecture one?

Densely connected neural networks Signup and view all the answers

How are the hidden layers in a densely connected network connected to each other?

Densely connected Signup and view all the answers

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

The image is transformed into a one-dimensional vector Signup and view all the answers

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

They lose the spatial structure of the image Signup and view all the answers

Why is it mentioned that densely connected networks have a ton of parameters?

Due to the dense connectivity between layers Signup and view all the answers

What is a key advantage of using convolutional neural networks for image processing compared to densely connected networks?

They preserve spatial information better Signup and view all the answers