Choosing the Right Architecture
"Best generative model" is a meaningless phrase. The best model depends on what you need from it. Here's a framework for choosing between GANs, VAEs, and diffusion models based on your actual requirements.
Answer these questions about your use case — we'll recommend the architecture that fits best.
Will a human visually inspect and judge the output quality?
Do you need to reason about or manipulate the latent space? (e.g. attribute editing, anomaly detection)
Do you need text-to-image generation or complex prompt-following?
Is training stability more important than getting the absolute sharpest outputs?
Is the task anomaly detection or density estimation?
Do you have a large compute budget and need state-of-the-art image quality?
Describe your use case and see which generative architecture best fits your requirements, with a reasoning explanation and known real-world deployments.
A security company wants to build an anomaly detection system for network traffic. They plan to train on normal traffic and flag anything that the model considers 'unlikely' as a potential attack. Which generative architecture is the best fit?