slimgroup/InvertibleNetworks.jl
A Julia framework for invertible neural networks
This tool helps researchers and practitioners in scientific computing and machine learning build neural networks that are memory-efficient and interpretable. It takes raw data or intermediate network activations and transforms them, providing both the forward result and the ability to perfectly reverse the process. Anyone working on tasks like uncertainty quantification, generative models, or image reconstruction with large datasets would find this useful.
169 stars. No commits in the last 6 months.
Use this if you need to build neural networks that are memory-efficient, allow for exact reconstruction of input data, and provide robust uncertainty estimates in scientific applications.
Not ideal if your primary goal is standard deep learning classification or regression without the specific need for invertibility or detailed uncertainty quantification.
Stars
169
Forks
24
Language
Julia
License
MIT
Category
Last pushed
Oct 01, 2025
Commits (30d)
0
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