Controlling Neural Levelsets

This repository contains an implementation to the Neurips 2019 paper Controlling Neural Level Sets.

This paper presents a simple and scalable approach to directly control level sets of a deep neural network. Our method consists of two parts: (i) sampling of the neural level sets, and (ii) relating the samples' positions to the network parameters. The latter is achieved by a sample network that is constructed by adding a single fixed linear layer to the original network. In turn, the sample network can be used to incorporate the level set samples into a loss function of interest. 

For more details visit: https://arxiv.org/abs/1905.11911.

Installation Requirmenets

The code is compatible with python 3.7 + pytorch 1.2. In addition, the following packages are required:
pyhocon, plotly, skimage, trimesh, pandas, advertorch, GPUtil, plyfile.

Usage

Robustness to adversarial examples:

cd ./code
python training_adv/exp_runner.py --conf ./confs/adv/[mnist_or_cifar]_ours.conf

jupyter notebook summerizing the results:

../monitoring/monitor_exps.ipynb

Surface reconstruciton:

• Download the faust dataset from http://faust.is.tue.mpg.de/

• According to downloaded path, adjust the variables in preprocess/faust.py

• Preprocessing faust dataset:

python preprocess/faust.py

• Training procedure for the surface reconstruciton task:

python training_recon/exp_runner.py --conf ./confs/recon/default.conf

• Generating meshes from the learned implicit representation, using the marching cubes algorithm:

python training_recon/post_plot_surface.py

• Outputs are saved in:

../exps/expname/[timestamp]/

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{atzmon2019controlling,
  title={Controlling neural level sets},
  author={Atzmon, Matan and Haim, Niv and Yariv, Lior and Israelov, Ofer and Maron, Haggai and Lipman, Yaron},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2032--2041},
  year={2019}
}