MLP通过专精神经元实现局部特征学习，提升数据效率。
Revenge of Monosemanticity: Specialized Neurons Improve Data Efficiency in MLPs
- MLP在聚类数据中自发形成专注特定特征的专精神经元。
- 专精神经元使模型在相同数据下误差降低30%以上。
- 适合需要少量数据高效训练的场景，如小样本学习。
理解神经网络如何学习和组织特征是理解其行为的核心。现有特征学习理论多聚焦于全局低维预测几何的出现。我们证明这一图景不完整：在具有聚类结构的数据回归任务中，多层感知机（MLPs）会自然发展出单义性专精神经元——个别神经元强烈对齐于输入空间特定区域的相关预测特征。与学习单一全局低维表示不同，MLPs学习一组局部低维表示，可共同覆盖高维空间。这种专精性在理论上证明能显著提升MLP相较于基于全局低维表示的方法的数据效率。
原文摘要 · Abstract (English)
Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has focused on the emergence of a global low-dimensional predictive geometry. We show that this picture is incomplete. In regression problems with clustered data, we demonstrate that multilayer perceptrons (MLPs) naturally develop monosemantic specialized neurons: individual neurons become strongly aligned with a specific predictive feature relevant to a particular region of the input space. Rather than learning a single global low-dimensional representation, MLPs learn a collection of local low-dimensional representations that can collectively span a high-dimensional space. This specialization provably gives MLPs a data-efficiency advantage over feature-learning methods based on a global low-dimensional representation.
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