This repository contains code to accompany the NeurIPS 2019 submission on Continual Unsupervised Representation Learning (CURL).
The experiments in the paper can be reproduced by running one of the three different training scripts:
train_sup.py: to run the supervised continual learning benchmark
train_unsup.py: to run the unsupervised i.i.d learning benchmark
train_main.py: to run all other experiments in the paper (with details in the
file on what to change)
In each of these cases, the cluster accuracy / purity and k-NN error are logged
to the terminal, and other quantities can be accessed from training.py
(e.g. the confusion matrix can be found in results['test_confusion']).
We recommend running these scripts in a Python virtual environment:
(Assuming python3-dev is installed in your system)
python3 -m venv .curl_venv
source .curl_venv/bin/activate
pip install wheel
pip install -r requirements.txt
PYTHONPATH=`pwd`/..:$PYTHONPATH python3 train_main.py --dataset='mnist'
Run `deactivate` to exit the virtual environment.