On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
This repository contains a simple implementation of Interval Bound Propagation (IBP) using TensorFlow: https://arxiv.org/abs/1810.12715
This is not an official Google product
IBP can be installed with the following command:
pip install git+https://github.com/deepmind/interval-bound-propagation`
IBP will work with both the CPU and GPU version of tensorflow and dm-sonnet, but to allow for that it does not list Tensorflow as a requirement, so you need to install Tensorflow and Sonnet separately if you haven't already done so.
This following command trains a small model on MNIST with epsilon set to 0.3:
cd examples python train.py --model=small --output_dir=/tmp/small_model
If you use this code in your work, we ask that you cite this paper:
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli. "On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models." arXiv preprint arXiv:1810.12715 (2018).