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Key Difference Discriminative vs Generative in Animal Classification?
Understand discriminative models’ cat-dog classification: learning separating features/boundaries from labeled data—vs. generative distributions—for AI certification exam clarity on model types.
Question
How does a discriminative model approach the task of distinguishing between images of cats and dogs?
A. It learns the statistical properties that define a cat or a dog.
B. It learns to generate a new image of a cat or a dog.
C. It focuses on features that best separate the two animal classes.
D. It requires an entirely new, unlabeled dataset.
Answer
C. It focuses on features that best separate the two animal classes.
Explanation
Discriminative models, including logistic regression, SVMs, and CNN classifiers, approach cat vs. dog image classification by learning a decision boundary in supervised fashion on labeled data, optimizing features like ear shape, snout width, fur texture, and eye spacing—extracted hierarchically through convolutional layers—that maximize class separability via cross-entropy loss or hinge loss on P(cat|image) vs. P(dog|image), directly modeling the posterior boundary without capturing full data distributions.
Option A describes generative modeling of class-conditional distributions. Option B defines generative tasks. Option D requires labeled data, not unlabeled.