Self-Taught Active Learning from Crowds

The emergence of social tagging and crowdsourcing systems provides a unique platform where multiple weak labelers can form a crowd to fulfill a labeling task. Yet crowd labelers are often noisy, inaccurate, and have limited labeling knowledge, and worst of all, they act independently without seeking complementary knowledge from each other to improve labeling performance. In this paper, we propose a Self-Taught Active Learning (STAL) paradigm, where imperfect labelers are able to learn complementary knowledge from one another to expand their knowledge sets and benefit the underlying active learner. We employ a probabilistic model to characterize the knowledge of each labeler through which a weak labeler can learn complementary knowledge from a stronger peer. As a result, the self-taught active learning process eventually helps achieve high classification accuracy with minimized labeling costs and labeling errors.