Having large amounts of annotated data significantly impacts the
effectiveness of deep neural networks. However, the annotation task can be very
expensive in some domains, such as medical data. Thus, it is important to
select the data to be annotated wisely, which is known as the subset selection
problem. We investigate the relationship between subset selection and neural
network pruning, which is more widely studied, and establish a correspondence
between them. Leveraging insights from network pruning, we propose utilizing
the norm criterion of neural network features to improve subset selection
methods. We empirically validate our proposed strategy on various networks and
datasets, demonstrating enhanced accuracy. This shows the potential of
employing pruning tools for subset selection.

在一些领域（如医学数据）中，有大量的标注数据对于深度神经网络的效果至关重要。为了明智地选择待标注的数据（即子集选择问题），我们研究了子集选择和神经网络剪枝之间的关系，并建立了它们之间的联系。借鉴了神经网络剪枝的见解，我们提出了利用神经网络特征的范数准则来改进子集选择方法。我们在各种网络和数据集上进行了实证验证，证明了准确度的提高。这表明利用剪枝工具进行子集选择的潜力。