  Training multiple-layered deep neural networks (DNNs) is difficult. The
standard practice of using a large number of samples for training often does
not improve the performance of a DNN to a satisfactory level. Thus, a
systematic training approach is needed. To address this need, we introduce an
innovative approach of using metamorphic relations (MRs) as "knowledge
carriers" to train DNNs. Based on the concept of metamorphic testing and MRs
(which play the role of a test oracle in software testing), we make use of the
notion of metamorphic group of inputs as concrete instances of MRs (which are
abstractions of knowledge) to train a DNN in a systematic and effective manner.
To verify the viability of our training approach, we have conducted a
preliminary experiment to compare the performance of two DNNs: one trained with
MRs and the other trained without MRs. We found that the DNN trained with MRs
has delivered a better performance, thereby confirming that our approach of
using MRs as knowledge carriers to train DNNs is promising. More work and
studies, however, are needed to solidify and leverage this approach to generate
widespread impact on effective DNN training.
