CNN with coarse-to-fine layer for hierarchical classification

Abstract

Most of the traditional convolution neural network (CNN)-based classification models are flat classifiers, which have an underlying assumption that all classes are equally difficult to distinguish. However, visual separability between different object categories is highly uneven in the real world. Recently, hierarchical classification has been proven effective for CNNs, more and more attempts have been made to exploit category hierarchies in CNN models. In this study, the authors propose a novel hierarchical CNN architecture, called coarse-to-fine CNN. It is simple, with a proposed coarse-to-fine layer on the top of a generic CNN. The coarse-to-fine layer is inspired by the Bayesian equation, where the coarse prediction can affect the fine prediction directly. Arbitrary CNNs can perform the hierarchical classification by adding the proposed layer. The training of a coarse-to-fine CNN is end-to-end, it can be optimised by typical stochastic gradient descent. In the test phase, it outputs multiple hierarchical predictions simultaneously. Experimental results on the benchmark datasets MNIST, CIFAR-10, and CIFAR-100 show clear advantages over the compared baselines.