Papers › Interpreting and Disentangling Feature Components of Various Complexity from DNNs
Interpreting and Disentangling Feature Components of Various Complexity from DNNs
Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang
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.
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