ALiPy swMATH ID: 33954 Software Authors: Ying-Peng Tang, Guo-Xiang Li, Sheng-Jun Huang Description: ALiPy: Active Learning in Python. Supervised machine learning methods usually require a large set of labeled examples for model training. However, in many real applications, there are plentiful unlabeled data but limited labeled data; and the acquisition of labels is costly. Active learning (AL) reduces the labeling cost by iteratively selecting the most valuable data to query their labels from the annotator. This article introduces a Python toobox ALiPy for active learning. ALiPy provides a module based implementation of active learning framework, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods. In the toolbox, multiple options are available for each component of the learning framework, including data process, active selection, label query, results visualization, etc. In addition to the implementations of more than 20 state-of-the-art active learning algorithms, ALiPy also supports users to easily configure and implement their own approaches under different active learning settings, such as AL for multi-label data, AL with noisy annotators, AL with different costs and so on. The toolbox is well-documented and open-source on Github, and can be easily installed through PyPI. Homepage: https://arxiv.org/abs/1901.03802 Source Code: https://github.com/NUAA-AL/ALiPy Dependencies: Python Keywords: Machine Learning; arXiv_cs.LG; arXiv_stat.ML; Active Learning; Python; Semi-Supervised Learning Related Software: Python; Scikit; PyTorch; modAL; ImageNet; libact; JCLAL; Cardinal; PyTorch Lightning; SuperGLUE; UCI-ml; scikit-activeml; skorch; BatchBALD; GPyTorch; Flax; JAX; Keras; PyRelationAL; lrtc Cited in: 1 Publication Standard Articles 1 Publication describing the Software Year ALiPy: Active Learning in Python Ying-Peng Tang, Guo-Xiang Li, Sheng-Jun Huang 2019 all top 5 Cited by 6 Authors 1 Cuomo, Salvatore 1 Gao, Kaifeng 1 Huo, Zenan 1 Mei, Gang 1 Piccialli, Francesco 1 Tu, Jingzhi Cited in 1 Serial 1 Computer Science Review Cited in 1 Field 1 Computer science (68-XX) Citations by Year