vislearn / FrEIA

Framework for Easily Invertible Architectures

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This is the Framework for Easily Invertible Architectures (FrEIA).

  • Construct Invertible Neural Networks (INNs) from simple invertible building blocks.
  • Quickly construct complex invertible computation graphs and INN topologies.
  • Forward and inverse computation guaranteed to work automatically.
  • Most common invertible transforms and operations are provided.
  • Easily add your own invertible transforms.

Papers

Our following papers use FrEIA, with links to code given below.

"Generative Classifiers as a Basis for Trustworthy Image Classification" (CVPR 2021)

"Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification" (Neurips 2020)

"Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (ICLR 2020)

"Guided Image Generation with Conditional Invertible Neural Networks" (2019)

"Analyzing inverse problems with invertible neural networks." (ICLR 2019)

Installation

FrEIA has the following dependencies:

Package Version
Python >= 3.7
Pytorch >= 1.0.0
Numpy >= 1.15.0
Scipy >= 1.5

Through pip

pip install FrEIA

Manually

For development:

# first clone the repository
git clone https://github.com/vislearn/FrEIA.git
cd FrEIA
# install the dependencies
pip install -r requirements.txt
# install in development mode, so that changes don't require a reinstall
python setup.py develop

Documentation

The full manual can be found at https://vislearn.github.io/FrEIA including

How to cite this repository

If you used this repository in your work, please cite it as below:

@software{freia,
  author = {Ardizzone, Lynton and Bungert, Till and Draxler, Felix and Köthe, Ullrich and Kruse, Jakob and Schmier, Robert and Sorrenson, Peter},
  title = {{Framework for Easily Invertible Architectures (FrEIA)}},
  year = {2018-2022},
  url = {https://github.com/vislearn/FrEIA}
}

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Framework for Easily Invertible Architectures

License:MIT License


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