Deep Learning Generative Models

Question 1

What are the two main components of a Generative Adversarial Network (GAN)?

Accepted Answer

Generator and discriminator trained in opposition to each other

Answer

A GAN consists of a generator that creates fake samples and a discriminator that distinguishes real from fake, with each trained adversarially to improve the other.

Question 2

What is mode collapse in GAN training?

Accepted Answer

The generator producing only a few distinct outputs rather than the full data distribution

Answer

Mode collapse occurs when the generator learns to produce a limited variety of outputs that fool the discriminator, ignoring most modes of the real data distribution.

Question 3

What is a Variational Autoencoder (VAE) and how does it differ from a standard autoencoder?

Accepted Answer

A VAE encodes inputs as probability distributions in latent space rather than fixed vectors, enabling principled sampling and generation

Answer

A VAE learns a probabilistic encoder mapping inputs to distributions (mean + variance) and uses the reparameterization trick to enable backpropagation through the sampling step.

Question 4

What does the reparameterization trick in VAEs enable?

Accepted Answer

Backpropagation through the stochastic sampling step by expressing samples as a deterministic function of parameters and a noise variable

Answer

The reparameterization trick writes z = μ + σ·ε (ε ~ N(0,1)), making the sample a differentiable function of μ and σ so gradients can flow through sampling.

Question 5

What is a diffusion model in the context of generative deep learning?

Accepted Answer

A generative model that learns to reverse a gradual noising process to generate data from pure noise

Answer

Diffusion models define a forward process that gradually adds noise to data and train a neural network to denoise, learning to generate data by iteratively denoising Gaussian noise.

Deep Learning Practice Test

Deep Learning Practice Test

Deep Learning Generative Models