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