Framework for Easily Invertible Architectures
Project description
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)
Paper: arxiv.org/abs/2001.04872
Code: github.com/VLL-HD/GIN
“Guided Image Generation with Conditional Invertible Neural Networks” (2019)
Paper: arxiv.org/abs/1907.02392
Supplement: drive.google.com/file/d/1_OoiIGhLeVJGaZFeBt0OWOq8ZCtiI7li
Code: github.com/VLL-HD/conditional_invertible_neural_networks
“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 git+https://github.com/VLL-HD/FrEIA.git
Manually
For development:
# first clone the repository
git clone https://github.com/VLL-HD/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://vll-hd.github.io/FrEIA including
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