iclr19-robust-locally-linear-nets

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iclr19-robust-locally-linear-nets

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"Towards Robust, Locally Linear Deep Networks" (ICLR 2019)

// repository documentation

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# Towards Robust, Locally Linear Deep Networks:

This repository is for the paper

* "[Towards Robust, Locally Linear Deep Networks](https://openreview.net/pdf?id=SylCrnCcFX)" by [Guang-He Lee](https://people.csail.mit.edu/guanghe/), [David Alvarez Melis](https://people.csail.mit.edu/davidam/), and [Tommi S. Jaakkola](http://people.csail.mit.edu/tommi/) in ICLR 19.
 * [Project page](http://people.csail.mit.edu/guanghe/locally_linear)

## Package version:

* PyTorch0.4.1
 * python3.6.1

## Reproducing the MNIST experiment:

* 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.

## Other experiments:

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

## Citation:

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},
}
```

iclr19-robust-locally-linear-nets

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