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Learning Structured Output Representation using Deep ...

Learning Structured Output Representationusing Deep Conditional Generative ModelsKihyuk Sohn Xinchen Yan Honglak Lee NEC Laboratories America, Inc. University of Michigan, Ann deep Learning has been successfully applied to many recognition prob-lems. Although it can approximate a complex many-to-one function well when alarge amount of training data is provided, it is still challenging to model com-plex Structured Output representations that effectively perform probabilistic infer-ence and make diverse predictions. In this work, we develop a deep conditionalgenerative model for Structured Output prediction using Gaussian latent model is trained efficiently in the framework of stochastic gradient varia-tional Bayes, and allows for fast prediction using stochastic feed-forward infer-ence.

set of latent variable z is generated from the prior distribution p (z) and the data x is generated by the generative distribution p (xjz) conditioned on z: z ˘p (z);x ˘p (xjz). In general, parameter estimation of directed graphical models is often challenging due to intractable posterior inference.

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