Can anyone Guide me on Advanced Generative Models: VAEs vs. Normalizing Flows?

Hello everyone,

I am currently delving into the fascinating world of generative models, specifically focusing on Variational Autoencoders (VAEs) and Normalizing Flows. While I have grasped the basic concepts of both, I am trying to understand the nuances and practical implications of their differences, especially in terms of the degrees of freedom they offer and their respective strengths and weaknesses.

From my understanding, VAEs are great for learning latent representations and generating data that resembles the training set. However, the trade off seems to be that they might not always produce the most accurate reconstructions due to the inherent stochasticity and the use of approximate inference techniques.

On the other hand, Normalizing Flows appear to provide exact likelihood computations and can model more complex data distributions due to their invertible transformations. This seems advantageous for certain applications, but I wonder if there are any downsides or specific scenarios where VAEs would be more beneficial despite their approximations.

Here are a few questions I have:

In terms of model flexibility and ability to capture intricate data distributions, how do VAEs and Normalizing Flows compare?
Are there particular use cases or types of data where one model significantly outperforms the other?
How do the training complexities and computational requirements of VAEs and Normalizing Flows differ in practice?
Can VAEs and Normalizing Flows be combined in any effective manner to leverage the strengths of both approaches?

Also, I have gone through this post; [[](](AWS DevOps Training | AWS DevOps Course Online - Updated [June 2024]) which definitely helped me out a lot.

I would greatly appreciate any insights, experiences, or resources you could share on this topic. Your expertise and advice will be incredibly valuable as I navigate this complex area of machine learning.

Thanks in advance for your help and assistance.