  Deep Active Learning (AL) techniques can be effective in reducing annotation
costs for training deep models. However, their effectiveness in low- and
high-budget scenarios seems to require different strategies, and achieving
optimal results across varying budget scenarios remains a challenge. In this
study, we introduce Dynamic Coverage & Margin mix (DCoM), a novel active
learning approach designed to bridge this gap. Unlike existing strategies, DCoM
dynamically adjusts its strategy, considering the competence of the current
model. Through theoretical analysis and empirical evaluations on diverse
datasets, including challenging computer vision tasks, we demonstrate DCoM's
ability to overcome the cold start problem and consistently improve results
across different budgetary constraints. Thus DCoM achieves state-of-the-art
performance in both low- and high-budget regimes.
