An implementation of the BADGE batch active learning algorithm. Details are provided in our paper, Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds, which was presented as a talk in ICLR 2020. This code was built by modifying Kuan-Hao Huang's deep active learning repository.
Update 1: We now understand BADGE to be an approximation of a more general algorithm, Batch Active Learning via Information maTrices (BAIT), which we published in NeurIPS 2021. The classification variant of BAIT has been added to this repository for completeness.
Update 2: It turns out that it's sometimes more natural to consider batch active learning in the streaming setting, instead of in a fixed-pool setting. If that's a better fit for your problem, check out this paper, published in ICML 2023, or the corresponding code.
To run this code fully, you'll need PyTorch (we're using version 1.11.0), scikit-learn, and OpenML. We've been running our code in Python 3.8.
python run.py --model resnet --nQuery 1000 --data CIFAR10 --alg badge
runs an active learning experiment using a ResNet and CIFAR-10 data, querying batches of 1,000 samples according to the BADGE algorithm.
This code allows you to also run each of the baseline algorithms used in our paper.
python run.py --model mlp --nQuery 10000 --did 6 --alg bait
runs an active learning experiment using an MLP and dataset number 6 from OpenML, querying batches of 10,000 with BAIT sampling.
Note that in our code, OpenML datasets can only be used with MLP architectures.
See the readme file in scripts/ for more details about generating plots like those in our paper.