Recently, several approaches have been proposed for decomposing deep neural network (DNN) classifiers into binary classifier modules to facilitate modular development and repair of such models. These approaches concern only the problem of decomposing classifier models, and some of them rely on the activation patterns of the neurons, thereby limiting their applicability.

In this paper, we propose a DNN decomposition technique, named Incite, that uses neuron mutation to quantify the contributions of the neurons to a given output of a model. Then, for each model output, a subgraph induced by the nodes with highest contribution scores for that output are selected and extracted as a module. Incite is agnostic to the type of the model and the activation functions used in its construction, and is applicable to not just classifiers, but to regression models as well. Furthermore, the costs of mutation analysis in Incite has been reduced by heuristic clustering of neurons, enabling its application to models with millions of parameters. Lastly, Incite prunes away the neurons that do not contribute to the outcome of the modules, producing compressed, efficient modules.

We have evaluated Incite using 16 DNN models for well-known classification and regression problems and report its effectiveness along combined accuracy (and MAE) of the modules, the overlap in model elements between the modules, and the compression ratio. We observed that, for classification models, Incite, on average, incurs 3.44% loss in accuracy, and the average overlap between the modules is 71.76%, while the average compression ratio is 1.89X. Meanwhile, for regression models, Incite, on average, incurs 18.56% gain in MAE, and the overlap between modules is 80.14%, while the average compression ratio is 1.83X.