  Understanding the internal representations of deep neural networks (DNNs) is
crucal to explain their behavior. The interpretation of individual units, which
are neurons in MLPs or convolution kernels in convolutional networks, has been
paid much attention given their fundamental role. However, recent research
(Morcos et al. 2018) presented a counterintuitive phenomenon, which suggests
that an individual unit with high class selectivity, called interpretable
units, has poor contributions to generalization of DNNs. In this work, we
provide a new perspective to understand this counterintuitive phenomenon, which
makes sense when we introduce Representative Substitution (RS). Instead of
individually selective units with classes, the RS refers to the independence of
a unit's representations in the same layer without any annotation. Our
experiments demonstrate that interpretable units have high RS which are not
critical to network's generalization. The RS provides new insights into the
interpretation of DNNs and suggests that we need to focus on the independence
and relationship of the representations.
