  A novel technique for deep learning of image classifiers is presented. The
learned CNN models offer better separation of deep features (also known as
embedded vectors) measured by Euclidean proximity and also no deterioration of
the classification results by class membership probability. The latter feature
can be used for enhancing image classifiers having the classes at the model's
exploiting stage different from from classes during the training stage. While
the Shannon information of SoftMax probability for target class is extended for
mini-batch by the intra-class variance, the trained network itself is extended
by the Hadamard layer with the parameters representing the class centers.
Contrary to the existing solutions, this extra neural layer enables interfacing
of the training algorithm to the standard stochastic gradient optimizers, e.g.
AdaM algorithm. Moreover, this approach makes the computed centroids
immediately adapting to the updating embedded vectors and finally getting the
comparable accuracy in less epochs.
