Taming Small-sample Bias in Low-budget Active Learning
Linxin Song Jieyu Zhang Xiaotian Lu Tianyi Zhou
Active learning (AL) aims to minimize the annotation cost by only querying a
few informative examples for each model training stage. However, training a
model on a few queried examples suffers from the small-sample bias. In this
paper, we address this small-sample bias issue in low-budget AL by exploring a
regularizer called Firth bias reduction, which can provably reduce the bias
during the model training process but might hinder learning if its coefficient
is not adaptive to the learning progress. Instead of tuning the coefficient for
each query round, which is sensitive and time-consuming, we propose the
curriculum Firth bias reduction (CHAIN) that can automatically adjust the
coefficient to be adaptive to the training process. Under both deep learning
and linear model settings, experiments on three benchmark datasets with several
widely used query strategies and hyperparameter searching methods show that
CHAIN can be used to build more efficient AL and can substantially improve the
progress made by each active learning query.