Seong Jin Cho

Active learning improves training efficiency by selectively querying the most informative samples for labeling. While it naturally fits …

Proposes a new active learning approach by proposing a new uncertainty measure called the least disagree metric, as well as its efficient estimator, which is proven to be asymptotically consistent. This is then combined with seeding to become a new active learning algorith, LDM-S, which is shown to outperform existing approaches across various architectures and datasets.