DeepAL+: Deep Active Learning Toolkit

DeepAL+ is a extended toolkit originated from DeepAL toolkit. Including 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]
Mean Standard Deviation [7]
Variation Ratios [8]
Cost-Effective Active Learning [9]
KMeans with scikit-learn library and with faiss-gpu library
Batch Active learning by Diverse Gradient Embeddings [10]
Loss Prediction Active Learning [11]
Variational Adversarial Active Learning [12]
Wasserstein Adversarial Active Learning [13]

We support 10 datasets, MNIST, FashionMNIST, EMNIST, SVHN, CIFAR10, CIFAR100, Tiny ImageNet, BreakHis, PneumoniaMNIST, Waterbirds. One can add new dataset by adding new function get_newdataset() in data.py.

Tiny ImageNet, BreakHis, PneumoniaMNIST need to be download manually, the corresponding data addresses could be found in data.py.

In DeepAL+, we use ResNet18 as basic classifier. One can replace it to other basic classifiers and add them to nets.py.

Prerequisites

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
openml 0.12.2
faiss-gpu 1.7.2
toma 1.1.0
opencv-python 4.5.5.64
wilds 2.0.0 (for waterbirds dataset only)

You can also use the following command to install conda environment

conda env create -f environment.yml

faiss-gpu and wilds should use pip install.

Demo

  python demo.py \
      -a RandomSampling \
      -s 100 \
      -q 1000 \
      -b 100 \
      -d MNIST \
      --seed 4666 \
      -t 3 \
      -g 0

See arguments.py for more instructions. We have also construct a comparative survey based on DeepAL+. Please refer here for more details.

Citing

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

@article{zhan2022comparative,
  title={A comparative survey of deep active learning},
  author={Zhan, Xueying and Wang, Qingzhong and Huang, Kuan-hao and Xiong, Haoyi and Dou, Dejing and Chan, Antoni B},
  journal={arXiv preprint arXiv:2203.13450},
  year={2022}
}

Reference

[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

[7] Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks, CVPR, 2016

[8] Elementary applied statistics: for students in behavioral science. New York: Wiley, 1965

[9] Cost-effective active learning for deep image classification. TCSVT, 2016

[10] Deep batch active learning by diverse, uncertain gradient lower bounds. ICLR, 2020

[11] Learning loss for active learning. CVPR, 2019

[12] Variational adversarial active learning, ICCV, 2019

[13] Deep active learning: Unified and principled method for query and training. AISTATS, 2020

Contact

If you have any further questions or want to discuss Active Learning with me or want to contribute your own Active Learning approaches into our toolkit, please contact xyzhan2-c@my.cityu.edu.hk (my spare email is sinezhan17@gmail.com).