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What are best practices for Variational Autoencoders (VAEs)

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Answer

When working with Variational Autoencoders (VAEs), start with a clear goal, test on real data early, keep the approach as simple as possible at first, and follow established practices from the AI community rather than guessing.

Real-world example A variational autoencoder can generate new handwritten digit images that look similar to a training set of digits.

Common follow-ups: What is Generative AI, How is Variational Autoencoders (VAEs) evaluated in practice, What tools are commonly used for Variational Autoencoders (VAEs)

Generative AI topics: Introduction to Generative AI Generative Adversarial Networks (GANs) Variational Autoencoders (VAEs)