Neural Network-based active learning (NAL) is a cost-effective data selection
technique that utilizes neural networks to select and train on a small subset
of samples. While existing work successfully develops various effective or
theory-justified NAL algorithms, the understanding of the two commonly used
query criteria of NAL: uncertainty-based and diversity-based, remains in its
infancy. In this work, we try to move one step forward by offering a unified
explanation for the success of both query criteria-based NAL from a feature
learning view. Specifically, we consider a feature-noise data model comprising
easy-to-learn or hard-to-learn features disrupted by noise, and conduct
analysis over 2-layer NN-based NALs in the pool-based scenario. We provably
show that both uncertainty-based and diversity-based NAL are inherently
amenable to one and the same principle, i.e., striving to prioritize samples
that contain yet-to-be-learned features. We further prove that this shared
principle is the key to their success-achieve small test error within a small
labeled set. Contrastingly, the strategy-free passive learning exhibits a large
test error due to the inadequate learning of yet-to-be-learned features,
necessitating resort to a significantly larger label complexity for a
sufficient test error reduction. Experimental results validate our findings.

神经网络主导的主动学习（NAL）是一种具有成本效益的数据选择技术，利用神经网络选择和训练一小部分样本。本研究在特征学习视角下，对两种常用的 NAL 查询准则：基于不确定性和基于多样性进行了统一解释，并证明这两种准则的成功都在于优先选择包含尚未学习特征的样本，从而在少量标记样本的情况下实现较小的测试误差。实验结果验证了我们的发现。