Your question is Compare GANs, VAEs, Diffusion. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are discussing model choices for a generative AI feature and need to explain how the main generative architectures differ. The goal is to compare how they generate samples, how they are trained, and where each one tends to work well or fail.
Explain the differences between generative AI architectures such as GANs, VAEs, and diffusion models. When would you choose one over another?