  A common practice in most of deep convolutional neural architectures is to
employ fully-connected layers followed by Softmax activation to minimize
cross-entropy loss for the sake of classification. Recent studies show that
substitution or addition of the Softmax objective to the cost functions of
support vector machines or linear discriminant analysis is highly beneficial to
improve the classification performance in hybrid neural networks. We propose a
novel paradigm to link the optimization of several hybrid objectives through
unified backpropagation. This highly alleviates the burden of extensive
boosting for independent objective functions or complex formulation of
multiobjective gradients. Hybrid loss functions are linked by basic probability
assignment from evidence theory. We conduct our experiments for a variety of
scenarios and standard datasets to evaluate the advantage of our proposed
unification approach to deliver consistent improvements into the classification
performance of deep convolutional neural networks.
