  In the domain of Active Learning (AL), a learner actively selects which
unlabeled examples to seek labels from an oracle, while operating within
predefined budget constraints. Importantly, it has been recently shown that
distinct query strategies are better suited for different conditions and
budgetary constraints. In practice, the determination of the most appropriate
AL strategy for a given situation remains an open problem. To tackle this
challenge, we propose a practical derivative-based method that dynamically
identifies the best strategy for a given budget. Intuitive motivation for our
approach is provided by the theoretical analysis of a simplified scenario. We
then introduce a method to dynamically select an AL strategy, which takes into
account the unique characteristics of the problem and the available budget.
Empirical results showcase the effectiveness of our approach across diverse
budgets and computer vision tasks.
