Seeking Advice on Designing an Invertible Neural Network for Fission

[wileewang]

First and foremost, I would like to express my sincere gratitude and respect for your work on this repository. The progress and innovations shared here have been immensely insightful and valuable to the community.

I am currently exploring the concept of fission in invertible neural networks, where a single latent representation 'x' can be decomposed into two distinct components 'y' and 'z'. My objective is to parameterize 'z' with a tractable distribution while ensuring that the combination of 'y' and 'z' can be accurately recombined to reconstruct 'x' using the reverse of the model.

Given your expertise in this field, I would greatly appreciate any guidance or suggestions you could provide on the following aspects:

1. Design Strategies: What are the best practices or strategies in designing such an invertible network that can effectively decompose and recombine representations?
2. Parameterization of 'z': How can 'z' be parameterized with a tractable distribution, and what are the implications of different distribution choices?
3. Ensuring Reversibility: What are the key considerations to ensure that the network remains reversible and accurate in the reconstruction phase?

Any insights, references, or examples you could share would be extremely helpful.

Thank you for your time and for the impactful contributions you've made to the field.

Best regards