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Active learning (AL) aims to minimize the annotation cost by only querying afew informative examples for each model training stage. However, training amodel on a few queried examples suffers from the small-sample bias. In thispaper, we address this small-sample bias issue in low-budget AL by exploring aregularizer called Firth bias reduction, which can provably reduce the biasduring the model training process but might hinder learning if its coefficientis not adaptive to the learning progress. Instead of tuning the coefficient foreach query round, which is sensitive and time-consuming, we propose thecurriculum Firth bias reduction (CHAIN) that can automatically adjust thecoefficient to be adaptive to the training process. Under both deep learningand linear model settings, experiments on three benchmark datasets with severalwidely used query strategies and hyperparameter searching methods show thatCHAIN can be used to build more efficient AL and can substantially improve theprogress made by each active learning query.

Taming Small-sample Bias in Low-budget Active Learning