  We propose a novel approach to enhance the discriminability of Convolutional
Neural Networks (CNN). The key idea is to build a tree structure that could
progressively learn fine-grained features to distinguish a subset of classes,
by learning features only among these classes. Such features are expected to be
more discriminative, compared to features learned for all the classes. We
develop a new algorithm to effectively learn the tree structure from a large
number of classes. Experiments on large-scale image classification tasks
demonstrate that our method could boost the performance of a given basic CNN
model. Our method is quite general, hence it can potentially be used in
combination with many other deep learning models.
