  In this paper, we present a deep neural network (DNN) training approach
called the "DeepMimic" training method. Enormous amounts of data are available
nowadays for training usage. Yet, only a tiny portion of these data is manually
labeled, whereas almost all of the data are unlabeled. The training approach
presented utilizes, in a most simplified manner, the unlabeled data to the
fullest, in order to achieve remarkable (classification) results. Our DeepMimic
method uses a small portion of labeled data and a large amount of unlabeled
data for the training process, as expected in a real-world scenario. It
consists of a mentor model and a student model. Employing a mentor model
trained on a small portion of the labeled data and then feeding it only with
unlabeled data, we show how to obtain a (simplified) student model that reaches
the same accuracy and loss as the mentor model, on the same test set, without
using any of the original data labels in the training of the student model. Our
experiments demonstrate that even on challenging classification tasks the
student network architecture can be simplified significantly with a minor
influence on the performance, i.e., we need not even know the original network
architecture of the mentor. In addition, the time required for training the
student model to reach the mentor's performance level is shorter, as a result
of a simplified architecture and more available data. The proposed method
highlights the disadvantages of regular supervised training and demonstrates
the benefits of a less traditional training approach.
