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The average accuracy is one of major evaluation metrics for classificationsystems, while the accuracy deviation is another important performance metricused to evaluate various deep neural networks. In this paper, we present a newensemble-like fast deep neural network, Harmony, that can reduce the accuracydeviation among categories without degrading overall average accuracy. Harmonyconsists of three sub-models, namely, Target model, Complementary model, andConductor model. In Harmony, an object is classified by using either Targetmodel or Complementary model. Target model is a conventional classificationnetwork for general categories, while Complementary model is a classificationnetwork especially for weak categories that are inaccurately classified byTarget model. Conductor model is used to select one of two models. Experimentalresults demonstrate that Harmony accurately classifies categories, while itreduces the accuracy deviation among the categories.

Deep ensemble network with explicit complementary model for accuracy-balanced classification