What does the decoder in a latent-variable model do?

• It turns hidden codes into data
• It compresses the training set into codes
• It scores how convincing a sample looks

When a generative model's job is to produce new samples, what is it judged by?

• The accuracy of the labels it predicts
• What it produces when sampled
• The speed at which it decodes a code

A latent-variable model with a continuous code and a neural decoder trains on a lower bound because its exact likelihood integrates over every possible code.

What does the lower bound give up compared with the true likelihood?

• Exactness, for an objective you can compute
• The latent codes, which the bound never sees
• The decoder, which the bound replaces

Two teams train on the same images. One only predicts labels for them, the other samples new ones. Which team must be using a generative model?

• The classifier, since labels are what it learned
• Both equally, data is data
• The sampler, it models the distribution

Two generators compete with no labels available. How do you compare them?

• Compare their accuracy on a held-out set of labelled images
• Rate samples with judges and compare held-out bounds
• Pick the one whose training loss finished lowest

A generator memorises training images pixel for pixel. Why do held-out bounds catch it?

• Memorised models fail on unseen data, which bounds measure
• Judges always spot a copied training image
• Large models cannot memorise individual images

A student maximises the bound and claims the likelihood rose equally. What is the error?

• Bounds always rise by exactly as much as likelihoods
• Likelihoods cannot rise once the bound has been maximised
• The gap can change, so the likelihood need not rise by the same amount