Deep Active Learning
Python implementations of the following active learning algorithms:
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