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Deep neural networks (DNNs) and decision trees (DTs) are bothstate-of-the-art classifiers. DNNs perform well due to their representationallearning capabilities, while DTs are computationally efficient as they performinference along one route (root-to-leaf) that is dependent on the input data.In this paper, we present DecisioNet (DN), a binary-tree structured neuralnetwork. We propose a systematic way to convert an existing DNN into a DN tocreate a lightweight version of the original model. DecisioNet takes the bestof both worlds - it uses neural modules to perform representational learningand utilizes its tree structure to perform only a portion of the computations.We evaluate various DN architectures, along with their corresponding baselinemodels on the FashionMNIST, CIFAR10, and CIFAR100 datasets. We show that the DNvariants achieve similar accuracy while significantly reducing thecomputational cost of the original network.

DecisioNet: A Binary-Tree Structured Neural Network