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 min and 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.
ECCV