Active learning enables the efficient construction of a labeled dataset by
labeling informative samples from an unlabeled dataset. In a real-world active
learning scenario, considering the diversity of the selected samples is crucial
because many redundant or highly similar samples exist. Core-set approach is
the promising diversity-based method selecting diverse samples based on the
distance between samples. However, the approach poorly performs compared to the
uncertainty-based approaches that select the most difficult samples where
neural models reveal low confidence. In this work, we analyze the feature space
through the lens of the density and, interestingly, observe that locally sparse
regions tend to have more informative samples than dense regions. Motivated by
our analysis, we empower the core-set approach with the density-awareness and
propose a density-aware core-set (DACS). The strategy is to estimate the
density of the unlabeled samples and select diverse samples mainly from sparse
regions. To reduce the computational bottlenecks in estimating the density, we
also introduce a new density approximation based on locality-sensitive hashing.
Experimental results clearly demonstrate the efficacy of DACS in both
classification and regression tasks and specifically show that DACS can produce
state-of-the-art performance in a practical scenario. Since DACS is weakly
dependent on neural architectures, we present a simple yet effective
combination method to show that the existing methods can be beneficially
combined with DACS.

通过密度感知和局部敏感哈希等方法，提出了一种基于多样性和难度的主动学习方法 (DACS)，可以通过选择含有更多信息的稀疏区域样本，有效地构建一个有标签的数据集，实验证明其能在分类和回归任务中实现同类最先进的性能。