Sum-Product Networks for Structured Prediction: Context-Specific Deep Conditional Random Fields
Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf · 2014
Linear-chain conditional random fields (LCCRFs) have been successfully applied in many structured prediction tasks. Many previous extensions, e.g. replacing local factors by neural networks, are computationally demanding. In this paper, we extend conventional LC-CRFs by replacing the local factors with sum-product networks, i.e. a promising new deep architecture allowing for exact and efficient inference. The proposed local factors can be interpreted as an extension of Gaussian mixture models (GMMs). Thus, we provide a powerful alternative to LC-CRFs extended by GMMs. In extensive experiments, we achieved performance competitive to state-ofthe-art methods in phone classification and optical character recognition tasks.