Python implementations of the following active learning algorithms:

• Random Sampling
• Least Confidence [1]
• Margin Sampling [2]
• Entropy Sampling [3]
• Uncertainty Sampling with Dropout Estimation [4]
• Bayesian Active Learning Disagreement [4]
• Core-Set Selection [5]
• Adversarial margin [6]

• 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

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 1000 \
      --n_init_labeled 10000 \
      --dataset_name MNIST \
      --strategy_name RandomSampling \
      --seed 1

Please refer here for more details.

If you use our code in your research or applications, please consider citing our paper.

@article{Huang2021deepal,
    author    = {Kuan-Hao Huang},
    title     = {DeepAL: Deep Active Learning in Python},
    journal   = {arXiv preprint arXiv:2111.15258},
    year      = {2021},
}

[1] A Sequential Algorithm for Training Text Classifiers, SIGIR, 1994

[2] Active Hidden Markov Models for Information Extraction, IDA, 2001

[3] Active learning literature survey. University of Wisconsin-Madison Department of Computer Sciences, 2009

[4] Deep Bayesian Active Learning with Image Data, ICML, 2017

[5] Active Learning for Convolutional Neural Networks: A Core-Set Approach, ICLR, 2018

[6] Adversarial Active Learning for Deep Networks: a Margin Based Approach, arXiv, 2018