Active_Learning.md

Active Learning

ALiPy

• link: https://github.com/NUAA-AL/ALiPy
• web: http://parnec.nuaa.edu.cn/huangsj/alipy/
• author: NUAA-AL (Ying-Peng Tang, Guo-Xiang Li, Sheng-Jun Huang)
• note: an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.

modAL

• link: https://github.com/modAL-python/modAL
• web: https://modal-python.github.io/
• author: modAL-python
• note: a modular active learning framework for Python3

libact

• link: https://github.com/ntucllab/libact
• author: NTUCSIE CLLab
• note: a pool-based active learning in python

deep-active-learning

• link: https://github.com/ej0cl6/deep-active-learning
• author: Kuan-Hao Huang
• note: Python implementations of the following algorithms: Random Sampling, Least Confidence, Margin Sampling, Entropy Sampling, Uncertainty Sampling with Dropout Estimation, Bayesian Active Learning Disagreement, Core-Set Selection, Adversarial margin.