NIPS Proceedingsβ

Discriminative Transfer Learning with Tree-based Priors

Part of: Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Authors

Conference Event Type: Poster

Abstract

This paper proposes a way of improving classification performance for classes which have very few training examples. The key idea is to discover classes which are similar and transfer knowledge among them. Our method organizes the classes into a tree hierarchy. The tree structure can be used to impose a generative prior over classification parameters. We show that these priors can be combined with discriminative models such as deep neural networks. Our method benefits from the power of discriminative training of deep neural networks, at the same time using tree-based generative priors over classification parameters. We also propose an algorithm for learning the underlying tree structure. This gives the model some flexibility to tune the tree so that the tree is pertinent to task being solved. We show that the model can transfer knowledge across related classes using fixed semantic trees. Moreover, it can learn new meaningful trees usually leading to improved performance. Our method achieves state-of-the-art classification results on the CIFAR-100 image data set and the MIR Flickr multimodal data set.