  In image classification, visual separability between different object
categories is highly uneven, and some categories are more difficult to
distinguish than others. Such difficult categories demand more dedicated
classifiers. However, existing deep convolutional neural networks (CNN) are
trained as flat N-way classifiers, and few efforts have been made to leverage
the hierarchical structure of categories. In this paper, we introduce
hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category
hierarchy. An HD-CNN separates easy classes using a coarse category classifier
while distinguishing difficult classes using fine category classifiers. During
HD-CNN training, component-wise pretraining is followed by global finetuning
with a multinomial logistic loss regularized by a coarse category consistency
term. In addition, conditional executions of fine category classifiers and
layer parameter compression make HD-CNNs scalable for large-scale visual
recognition. We achieve state-of-the-art results on both CIFAR100 and
large-scale ImageNet 1000-class benchmark datasets. In our experiments, we
build up three different HD-CNNs and they lower the top-1 error of the standard
CNNs by 2.65%, 3.1% and 1.1%, respectively.
