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Nearest-Neighbor Un-embedding
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A data scientist is developing a model to classify images of animals into different categories: cats, dogs, and birds. They decide to use nearest-neighbor un-embedding to improve the accuracy of their classifications. If they have embedded the classes as vectors, what should they primarily focus on to effectively classify a new image?

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Choose the Best Answer

A

Calculating the signed distances from the new image vector to each class vector

B

Using the average color of the images to classify

C

Counting the number of pixels in each category

D

Randomly selecting a class for the new image

Understanding the Answer

Let's break down why this is correct

Answer

The scientist must first embed the new image into the same vector space as the class vectors, using the same feature extractor and dimensionality. Then they should compute a distance or similarity score between this image vector and each of the class vectors, using a metric that reflects the geometry of the space (for example Euclidean or cosine distance). The class whose vector is closest to the image vector is chosen as the prediction. For instance, if the image of a golden retriever is embedded to (0. 8, 0.

Detailed Explanation

The goal is to find the class whose vector is closest to the new image vector. Other options are incorrect because Using only the average color ignores the vector representation of the classes; Counting pixels does not measure similarity in the vector space.

Key Concepts

Nearest-neighbor un-embedding
Multi-class classification
Vector representation
Topic

Nearest-Neighbor Un-embedding

Difficulty

easy level question

Cognitive Level

understand

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Question 1

In nearest-neighbor un-embedding, the process of determining the closest vector to a given prediction relies on calculating ____ to decision boundaries for effective classification.

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