Homogeneous Linear Inequality Constraints for Neural Network Activations

This repository contains a Python implementation of the algorithm presented in our paper:

Homogeneous Linear Inequality Constraints for Neural Network Activations
Thomas Frerix, Matthias Nießner, Daniel Cremers
CVPR 2020 Deep Vision Workshop

Installation

1. Make sure you have a running Python 3 (tested with Python 3.7) ecosytem, e.g., through conda, and an Nvidia GPU (tested with CUDA 10.1 on a Titan X)
2. Install pytorch, e.g., conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
3. Install the other dependencies via pip install -r requirements.txt

Example

Execute the file example.sh to make a sample run of our algorithm. In this toy experiment, the algorithm learns an orthogonal projection onto a checkerboard constraint. The results directory will contain MNIST test set samples and the output of the trained model.

Paper Reference

@InProceedings{Frerix2020,
    author = {Frerix, Thomas and Nie{\ss}ner, Matthias and Cremers, Daniel},
    title = {Homogeneous Linear Inequality Constraints for Neural Network Activations},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    year = {2020}
}