https://github.com/ej0cl6/deep-active-learning

Deep Active Learning
https://github.com/ej0cl6/deep-active-learning

active-learning deep-active-learning

Last synced: 2 months ago
JSON representation

Deep Active Learning

• Host: GitHub
• URL: https://github.com/ej0cl6/deep-active-learning
• Owner: ej0cl6
• License: mit
• Created: 2018-05-03T05:51:17.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-10-03T20:23:38.000Z (over 1 year ago)
• Last Synced: 2024-01-25T21:06:30.039Z (5 months ago)
• Topics: active-learning, deep-active-learning
• Language: Python
• Homepage:
• Size: 40 KB
• Stars: 741
• Watchers: 16
• Forks: 177
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-stars - ej0cl6/deep-active-learning - Deep Active Learning (Python)
• awesome-machine-learning-resources - **[Code Collection - active-learning?style=social) (Table of Contents)
• awesome-active-learning - DeepAL - learn, pytorch) | Kuan-Hao Huang | Keep updating & deep neural networks | (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 in Python

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]

- Cluster-Based Selection [5]

- Adversarial margin [6]

## 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

You can also use the following command to install conda environment

```

conda env create -f environment.yml

```

## Demo 

```

  python demo.py \

      --n_round 10 \

      --n_query 1000 \

      --n_init_labeled 10000 \

      --dataset_name MNIST \

      --strategy_name RandomSampling \

      --seed 1

```

Please refer [here](https://arxiv.org/abs/2111.15258) for more details.

## Citing

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},

}

```

## 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