BlackHC/BatchBALD

Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.

BatchBALD

Note: A more modular re-implementation can be found at https://github.com/BlackHC/batchbald_redux.

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This is the code drop for our paper BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.

The code comes as is.

See https://github.com/BlackHC/batchbald_redux and https://blackhc.github.io/batchbald_redux/ for a reimplementation.

ElementAI's Baal framework also supports BatchBALD: https://github.com/ElementAI/baal/.

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}
}

How to run it

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.py --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!