Principled Hybrids of Generative and Discriminative Models

When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, for large-scale applications such as object recognition, hand labelling of data is expensive, and there is much interest in semi-supervised techniques based on generative models in which the majority of the training data is unlabelled. Although the generalization performance of generative models can often be improved by `training them discriminatively', they can then no longer make use of unlabelled data. In an attempt to gain the benefit of both generative and discriminative approaches, heuristic procedure have been proposed [2, 3] which interpolate between these two extremes by taking a convex combination of the generative and discriminative objective functions. In this paper we adopt a new perspective which says that there is only one correct way to train a given model, and that a `discriminatively trained' generative model is ...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Julia A. Lasserre, Christopher M. Bishop, Thomas P. Minka
• Keywords: Computer Vision, CVPR 2006, Discriminative Extremes, Discriminative Models, Discriminative Objective Functions, Discriminative Techniques, Discriminative Training