What is the minimax objective in a GAN?

The Generator seeks to minimize the probability that the Discriminator correctly identifies fakes, while the Discriminator seeks to maximize its detection accuracy.

What is the difference between an Autoencoder and a VAE?

Standard autoencoders map inputs to fixed discrete latent points, which may leave gaps. VAEs map inputs to continuous probability distributions, allowing smooth sampling of new data.

Home ASSEB AI HubUnit 3: Generative AI

Class XIIASSEB-527Unit 3: Generative AI

Unit 3: Generative AI

Master official ASSEB Class XII AI Unit 3: Generative vs Discriminative AI, Autoencoders, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Large Language Models (LLMs).

Theory: 9 Marks

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Practical: 5 Marks

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11 min read

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Learning Outcomes

Distinguish between Discriminative models ($P(Y|X)$) and Generative models ($P(X)$).
Understand Autoencoders and Variational Autoencoders (VAEs): latent bottlenecks and probabilistic sampling.
Analyze Generative Adversarial Networks (GANs): Generator network, Discriminator network, and minimax game objective.
Explore Large Language Models (LLMs): pretraining, autoregressive next-token prediction, and prompt engineering.
Implement a Python script using pre-trained generative models to synthesize novel images and text.

Board Exam Scoring Tips

In board exams, draw the classic GAN block diagram: Real Data + Noise -> Generator & Discriminator -> Real/Fake classification -> Loss feedback.
Clearly contrast Discriminative vs. Generative models: Discriminative predicts class labels, Generative creates new instances.
Cite real-world applications: medical image data augmentation, creative design, and synthetic audio generation.

Key Takeaways

Discriminative models separate classes; Generative models learn the underlying probability distribution to generate new, synthetic data samples.
In a GAN, the Generator tries to fool the Discriminator, while the Discriminator tries to distinguish real training samples from fake generated ones.
Autoencoders compress high-dimensional inputs into a bottleneck latent space and reconstruct them; VAEs constrain latent vectors to standard Gaussian distributions.
LLMs are massive Transformer decoders trained on internet-scale text to predict the most probable next token.
Unit 3 carries 20 periods and 7 theory marks in the official ASSEB curriculum, reflecting the importance of modern GenAI.

Practice Unit MCQ Quiz

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