A supplementary code for Editable Neural Networks, an ICLR 2020 submission by Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Pyrkin, Sergei Popov, Artem Babenko.

It trains a model so that it can later be edited: forced to predict a specific class on a specific input without losing accuracy.

• A machine with some CPU (preferably 2+ free cores) and GPU(s)
  • Running without GPU is possible but does not scale well, especially for ImageNet
• Some popular Linux x64 distribution
  • Tested on Ubuntu16.04, should work fine on any popular linux64 and even MacOS;
  • Windows and x32 systems may require heavy wizardry to run;
  • When in doubt, use Docker, preferably GPU-enabled (i.e. nvidia-docker)

1. Clone or download this repo. cd yourself to it's root directory.
2. Grab or build a working python enviromnent. Anaconda works fine.
3. Install packages from requirements.txt
4. Run jupyter notebook and open a notebook in ./notebooks/

• Before you run the first cell, change %env CUDA_VISIBLE_DEVICES=# to an index that you plan to use.
• CIFAR10 notebook can be ran with no extra preparation
• The ImageNet notebooks require a step-by-step procedure to get running:
  1. Download the dataset first. See this page or just google it. No, really, go google it!
  2. Run imagenet_preprocess_logits.ipynb
  3. Train with imagenet_editable_training.ipynb
  4. Evaluate by using one of the two remaining notebooks.
• To reproduce machine translation experiments, follow the instructions in ./mt/README.md