This repository is for the paper

• "Towards Robust, Locally Linear Deep Networks" by Guang-He Lee, David Alvarez Melis, and Tommi S. Jaakkola in ICLR 19.
• Project page

• PyTorch0.4.1
• python3.6.1

• Please execute the shell files (gamma100_fc.sh) to reproduce the experiment on MNIST dataset with gamma=100. The results will be in the folder fc_log/

• parse_log.py is a utility script. After you run all the models using gamma100_fc.sh. Use the following comment:

  • ls fc_log > fc_log.list
  • cd fc_log
  • python ../parse_log.py --file-list ../fc_log.list

• To inspect the best model in terms of the median of L2 margin given each validation accuracy, please look into the log file to see the testing scores of the model.

• Unfortunately we don't plan to release the codes for other experiments.

If you find this repo useful for your research, please cite the paper

@inproceedings{
  lee2018towards,
  title={Towards Robust, Locally Linear Deep Networks},
  author={Guang-He Lee and David Alvarez-Melis and Tommi S. Jaakkola},
  booktitle={International Conference on Learning Representations},
  year={2019},
  url={https://openreview.net/forum?id=SylCrnCcFX},
}