This is the code drop for our paper BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.
The code comes as is.
Please cite us:
@misc{kirsch2019batchbald,
title={BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning},
author={Andreas Kirsch and Joost van Amersfoort and Yarin Gal},
year={2019},
eprint={1906.08158},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Make sure you install all requirements using
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
pip install -r requirements.txt
and you can start an experiment using:
python src/run_experiment --quickquick --num_inference_samples 10 --available_sample_k 40
which starts an experiment on a subset of MNIST with 10 MC dropout samples and acquisition size 40.
Have fun playing around with it!
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