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https://github.com/SineZHAN/deepALplus

This is a toolbox for Deep Active Learning, an extension from previous work https://github.com/ej0cl6/deep-active-learning (DeepAL toolbox).
https://github.com/SineZHAN/deepALplus

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This is a toolbox for Deep Active Learning, an extension from previous work https://github.com/ej0cl6/deep-active-learning (DeepAL toolbox).

Lists

README 

        
# DeepAL+: Deep Active Learning Toolkit



DeepAL+ is an extended toolkit originated from [DeepAL toolkit](https://github.com/ej0cl6/deep-active-learning). 

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 a new dataset by adding a new function `get_newdataset()` in `data.py`.



*Tiny ImageNet*, *BreakHis*, *PneumoniaMNIST* need to be downloaded manually, the corresponding data addresses can be found in `data.py`.



In DeepAL+, we use **ResNet18** as the basic classifier. One can replace it with 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 the 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 constructed a comparative survey based on DeepAL+. 

Please refer to [here](https://arxiv.org/pdf/2203.13450.pdf) for more details.



## Citing



Please consider citing our paper if you use our code in your research or applications.



```

@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 contribute your own Active Learning approaches to our toolkit, please contact [email protected] (my spare email is [email protected]).