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.
翻译:积极学习有助于高效构建标签式数据集。 在现实世界积极学习的情景中,考虑到所选样本的多样性,因为存在许多冗余或非常相似的样本。核心设定方法是充满希望的基于多样性的方法,根据样本之间的距离选择不同样本。然而,与基于不确定性的方法相比,该方法表现不佳,后者选择的是神经模型显示信心低的最困难的样本。在这项工作中,我们通过密度透镜分析特征空间,令人感兴趣的是,发现本地稀疏区域往往拥有比稠密区域更多的信息样本。根据我们的分析,我们以密度意识为动力,赋予核心设定方法以核心设定方法以密度意识,并提议一个密度认知核心设置(DACS)。 战略是估计未贴标签样本的密度和主要来自稀疏地区的不同样本。为了减少估算密度的计算瓶颈,我们还采用了基于对地感敏感的存储点的新的密度近似度。实验结果清楚地表明,DACS在分类和回归任务中都具有更高的效率,并具体表明DACS能够以可靠的方式生成一种实用的模型。