Deep Ensemble Network with Explicit Complementary Model for Accuracy-balanced Classification

Dohyun Kim, Joongheon Kim

http://doi.org/10.5626/JOK.2019.46.9.941

One of the major evaluation metrics for classification systems is average accuracy, while accuracy deviation is another important performance metric used to evaluate various deep neural networks. In this paper, we present a new ensemble-like fast deep neural network, Harmony, that can reduce the accuracy deviation among categories without degrading the overall average accuracy. Harmony consists of three sub-models: the Target model, Complementary model, and Conductor model. In Harmony, an object is classified by using either the Target model or the Complementary model. The Target model is a conventional classification network for general categories, while the Complementary model is a classification network specifically for weak categories that are inaccurately classified by the Target model. The Conductor model is used to select one of the two models. The experimental results indicate that Harmony accurately classifies categories and also, reduces the accuracy deviation among the categories.