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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
Last synced: about 2 months ago
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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).
- Host: GitHub
- URL: https://github.com/SineZHAN/deepALplus
- Owner: SineZHAN
- License: mit
- Created: 2022-06-08T02:55:55.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-05-06T20:28:34.000Z (8 months ago)
- Last Synced: 2024-10-29T12:57:46.289Z (2 months ago)
- Language: Python
- Homepage:
- Size: 56.6 KB
- Stars: 170
- Watchers: 3
- Forks: 24
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-active-learning - DeepAL+ - learn, pytorch) | Zhan | An extension for DeepAL | (3.3 AL in AI Fields - 人工智能背景中的主动学习 / **Tutorials - 教程**)
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]).