ActiveDP: Bridging Active Learning and Data Programming
Naiqing Guan Nick Koudas
Modern machine learning models require large labelled datasets to achieve
good performance, but manually labelling large datasets is expensive and
time-consuming. The data programming paradigm enables users to label large
datasets efficiently but produces noisy labels, which deteriorates the
downstream model's performance. The active learning paradigm, on the other
hand, can acquire accurate labels but only for a small fraction of instances.
In this paper, we propose ActiveDP, an interactive framework bridging active
learning and data programming together to generate labels with both high
accuracy and coverage, combining the strengths of both paradigms. Experiments
show that ActiveDP outperforms previous weak supervision and active learning
approaches and consistently performs well under different labelling budgets.