Online meta-learned gradient norms for active learning in science and technology

Acquisition of scientific data can be expensive and time-consuming. Active learning is a solution to reduce costs and time by guiding the selection of scientific experiments. Autonomous and automatic identification of the most essential samples to annotate by active learning can also help to mitigate human bias. Previous research has demonstrated that unlabelled samples causing the largest gradient norms of neural network models can promote active learning in classification. However, gradient norm estimation in regression is non-trivial because the continuous one-dimensional output of regression significantly differs from classification. In this study, we propose a new active learning method that uses meta-learning to estimate the gradient norm of the unlabelled sample in regression. Specifically, we use a separate model to be a selector that learns knowledge from the previous active learning results and is used to predict the gradient norms of unlabelled samples. In each active learning iteration, we estimate and select unlabelled samples with the largest gradient norms to annotate. Our method is evaluated on six regression data sets in various domains, which include costly scientific data.