This paper presents a phenomenon in neural networks that we refer to as textit{local elasticity}. Roughly speaking, a classifier is said to be locally elastic if its prediction at a feature vector $bx$ is textit{not} significantly perturbed, after the classifier is updated via stochastic gradient descent at a (labeled) feature vector $bx$ that is textit{dissimilar} to $bx$ in a certain sense. This phenomenon is shown to persist for neural networks with nonlinear activation functions through extensive simulations on real-life and synthetic datasets, whereas this is not observed in linear classifiers. In addition, we offer a geometric interpretation of local elasticity using the neural tangent kernel citep{jacot2018neural}. Building on top of local elasticity, we obtain pairwise similarity measures between feature vectors, which can be used for clustering in conjunction with $K$-means. The effectiveness of the clustering algorithm on the MNIST and CIFAR-10 datasets in turn corroborates the hypothesis of local elasticity of neural networks on real-life data. Finally, we discuss some implications of local elasticity to shed light on several intriguing aspects of deep neural networks.
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