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BaaL

swMATH ID: 33568
Software Authors: Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste
Description: Bayesian active learning for production, a systematic study and a reusable library. Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific inputs. While there are many papers on new active learning techniques, these techniques rarely satisfy the constraints of a real-world project. In this paper, we analyse the main drawbacks of current active learning techniques and we present approaches to alleviate them. We do a systematic study on the effects of the most common issues of real-world datasets on the deep active learning process: model convergence, annotation error, and dataset imbalance. We derive two techniques that can speed up the active learning loop such as partial uncertainty sampling and larger query size. Finally, we present our open-source Bayesian active learning library, BaaL.
Homepage: https://baal.readthedocs.io/en/latest/
Source Code: https://github.com/ElementAI/baal
Keywords: Machine Learning; arXiv_cs.LG; arXiv_stat.ML; Bayesian active learning library; Active learning
Related Software: PyTorch; NumPy; BatchBALD; ImageNet
Cited in: 0 Publications

Standard Articles

1 Publication describing the Software Year
Bayesian active learning for production, a systematic study and a reusable library
Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste
2020