  While deep neural networks extract rich features from the input data, the
current trade-off between depth and computational cost makes it difficult to
adopt deep neural networks for many industrial applications, especially when
computing power is limited. Here, we are inspired by the idea that, while
deeper embeddings are needed to discriminate difficult samples (i.e.,
fine-grained discrimination), a large number of samples can be well
discriminated via much shallower embeddings (i.e., coarse-grained
discrimination). In this study, we introduce the simple yet effective concept
of decision gates (d-gate), modules trained to decide whether a sample needs to
be projected into a deeper embedding or if an early prediction can be made at
the d-gate, thus enabling the computation of dynamic representations at
different depths. The proposed d-gate modules can be integrated with any deep
neural network and reduces the average computational cost of the deep neural
networks while maintaining modeling accuracy. The proposed d-gate framework is
examined via different network architectures and datasets, with experimental
results showing that leveraging the proposed d-gate modules led to a ~43%
speed-up and 44% FLOPs reduction on ResNet-101 and 55% speed-up and 39% FLOPs
reduction on DenseNet-201 trained on the CIFAR10 dataset with only ~2% drop in
accuracy. Furthermore, experiments where d-gate modules are integrated into
ResNet-101 trained on the ImageNet dataset demonstrate that it is possible to
reduce the computational cost of the network by 1.5 GFLOPs without any drop in
the modeling accuracy.
