So far, I’ve written about three types of generative models, GAN, VAE, and Flow-based models. They have shown great success in generating high-quality samples, but each has some limitations of its own. GAN models are known for potentially unstable training and less diversity in generation due to their adversarial training nature. VAE relies on a surrogate loss. Flow models have to use specialized architectures to construct reversible transform.

Do you have experience with GANs/Diffusion based models such as Stable Diffusion, Midjourney etc?*Your answer

Generative AI is a type of artificial intelligence that can ____.Generate text, images, or other data using generative models.Make predictions about future events.Generate responses on real-time data.Perform complex calculations and mathematical operations.

What is important to understand about how generative AI models work?The model uses small amounts of specific data.The model needs constant management.The generated results should be fact-checked.The generated results can be taken at face value.I don't know this yet.

In the formulation of Generative Adversarial Networks (GANs), which term represents the objective of the generator to match the distribution of generated samples to that of real data?Question 8Answera.Generator lossb.Adversarial lossc. Reconstruction lossd.Discriminator loss

Which architecture can help address convergence issues in traditional GANs?RNNDBNWGANCNNLSTM