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About this interview
A technical interview on Deep Generative Models, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain the VAE objective (ELBO) and the reparameterization trick
Explain GAN training (minimax game) and failure modes like mode collapse
Compare GANs, VAEs, and diffusion models on fidelity, diversity, and stability
Describe how generative models are evaluated (FID, IS)
Topics covered
Generative vs DiscriminativeVAE FundamentalsGAN FundamentalsVAE Latent SpaceVAE ELBOVAE TrainingGAN TrainingGAN Failure ModesGAN VariantsEvaluationVAE vs GANVAE Failure ModesVAE VariantsDiffusion Models
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“At a high level, what is the goal of a generative model, and how does it differ from a discriminative model?
“What is the Lipschitz constraint in WGAN, and why does WGAN-GP enforce it with a gradient penalty rather than weight clipping?
“What is classifier-free guidance in diffusion models, and how does it let you trade off sample quality against diversity?