论文标题
解释和解开DNN各种复杂性的特征组成部分
Interpreting and Disentangling Feature Components of Various Complexity from DNNs
论文作者
论文摘要
本文旨在定义，量化和分析DNN学到的特征复杂性。我们为特征复杂性提出了一个通用定义。鉴于DNN中某个层的特征，我们的方法DISANGLES具有与该功能不同复杂性顺序的组件。我们进一步设计了一组指标来评估这些特征组件过度拟合的可靠性，有效性和重要性。此外，我们成功地发现了特征复杂性与DNN的性能之间的密切关系。作为一种通用的数学工具，特征复杂性和所提出的指标也可以用于分析网络压缩和知识蒸馏的成功。
This paper aims to define, quantify, and analyze the feature complexity that is learned by a DNN. We propose a generic definition for the feature complexity. Given the feature of a certain layer in the DNN, our method disentangles feature components of different complexity orders from the feature. We further design a set of metrics to evaluate the reliability, the effectiveness, and the significance of over-fitting of these feature components. Furthermore, we successfully discover a close relationship between the feature complexity and the performance of DNNs. As a generic mathematical tool, the feature complexity and the proposed metrics can also be used to analyze the success of network compression and knowledge distillation.
