Taxonomy-Regularized Semantic Deep Convolutional Neural Networks

We propose a novel convolutional network architecture that abstracts and differentiates the categories based on a given class hierarchy. We exploit grouped and discriminative information provided by the taxonomy, by focusing on the general and specific components that comprise each category, through the minand difference-pooling operations. Without using any additional parameters or substantial increase in time complexity, our model is able to learn the features that are discriminative for classifying often confused sub-classes belonging to the same superclass, and thus improve the overall classification performance. We validate our method on CIFAR-100, Places-205, and ImageNet Animal datasets, on which our model obtains significant improvements over the base convolutional networks.