Python implementations of the following active learning algorithms:
- Bayesian Active Learning Disagreement [1]
- numpy 1.21.2
- scipy 1.7.1
- pytorch 1.10.0
- torchvision 0.11.1
- scikit-learn 1.0.1
- tqdm 4.62.3
- ipdb 0.13.9
- pandas 1.4.4
You can also use the following command to install conda environment
conda env create -f environment.yml
python demo.py \
--n_round 10 \
--n_query 100 \
--n_init_labeled 10000 \
--dataset_name MNIST \
--strategy_name BALDDropout \
--seed 1
Please refer here for more details.
Forked from:
@article{Huang2021deepal,
author = {Kuan-Hao Huang},
title = {DeepAL: Deep Active Learning in Python},
journal = {arXiv preprint arXiv:2111.15258},
year = {2021},
}
[1] Deep Bayesian Active Learning with Image Data, ICML, 2017