  In this paper, we propose an elegant solution that is directly addressing the
bottlenecks of the traditional deep learning approaches and offers a clearly
explainable internal architecture that can outperform the existing methods,
requires very little computational resources (no need for GPUs) and short
training times (in the order of seconds). The proposed approach, xDNN is using
prototypes. Prototypes are actual training data samples (images), which are
local peaks of the empirical data distribution called typicality as well as of
the data density. This generative model is identified in a closed form and
equates to the pdf but is derived automatically and entirely from the training
data with no user- or problem-specific thresholds, parameters or intervention.
The proposed xDNN offers a new deep learning architecture that combines
reasoning and learning in a synergy. It is non-iterative and non-parametric,
which explains its efficiency in terms of time and computational resources.
From the user perspective, the proposed approach is clearly understandable to
human users. We tested it on some well-known benchmark data sets such as iRoads
and Caltech-256. xDNN outperforms the other methods including deep learning in
terms of accuracy, time to train and offers a clearly explainable classifier.
In fact, the result on the very hard Caltech-256 problem (which has 257
classes) represents a world record.
