  While deep neural networks have succeeded in several visual applications,
such as object recognition, detection, and localization, by reaching very high
classification accuracies, it is important to note that many real-world
applications demand varying costs for different types of misclassification
errors, thus requiring cost-sensitive classification algorithms. Current models
of deep neural networks for cost-sensitive classification are restricted to
some specific network structures and limited depth. In this paper, we propose a
novel framework that can be applied to deep neural networks with any structure
to facilitate their learning of meaningful representations for cost-sensitive
classification problems. Furthermore, the framework allows end-to-end training
of deeper networks directly. The framework is designed by augmenting auxiliary
neurons to the output of each hidden layer for layer-wise cost estimation, and
including the total estimation loss within the optimization objective.
Experimental results on public benchmark visual data sets with two cost
information settings demonstrate that the proposed framework outperforms
state-of-the-art cost-sensitive deep learning models.
