  Regularization can mitigate the generalization gap between training and
inference by introducing inductive bias. Existing works have already proposed
various inductive biases from diverse perspectives. However, none of them
explores inductive bias from the perspective of class-dependent response
distribution of individual neurons. In this paper, we conduct a substantial
analysis of the characteristics of such distribution. Based on the analysis
results, we articulate the Neuron Steadiness Hypothesis: the neuron with
similar responses to instances of the same class leads to better
generalization. Accordingly, we propose a new regularization method called
Neuron Steadiness Regularization (NSR) to reduce neuron intra-class response
variance. Based on the Complexity Measure, we theoretically guarantee the
effectiveness of NSR for improving generalization. We conduct extensive
experiments on Multilayer Perceptron, Convolutional Neural Networks, and Graph
Neural Networks with popular benchmark datasets of diverse domains, which show
that our Neuron Steadiness Regularization consistently outperforms the vanilla
version of models with significant gain and low additional computational
overhead.
