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Batch Active learning by Diverse Gradient Embeddings (BADGE)

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.

Dependencies

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.

Running an experiment

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.

Analyzing experimental results

See the readme file in scripts/ for more details about generating plots like those in our paper.