  Deep neural networks have achieved state-of-art performance in many domains
including computer vision, natural language processing and self-driving cars.
However, they are very computationally expensive and memory intensive which
raises significant challenges when it comes to deploy or train them on strict
latency applications or resource-limited environments. As a result, many
attempts have been introduced to accelerate and compress deep learning models,
however the majority were not able to maintain the same accuracy of the
baseline models. In this paper, we describe EnSyth, a deep learning ensemble
approach to enhance the predictability of compact neural network's models.
First, we generate a set of diverse compressed deep learning models using
different hyperparameters for a pruning method, after that we utilise ensemble
learning to synthesise the outputs of the compressed models to compose a new
pool of classifiers. Finally, we apply backward elimination on the generated
pool to explore the best performing combinations of models. On CIFAR-10,
CIFAR-5 data-sets with LeNet-5, EnSyth outperforms the predictability of the
baseline model.
