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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.

Along with the recent breakthroughs in supervised deep learning methods, there has been a progress in deep generative models, such as deep belief networks [10,20] and deep Boltzmann machines [25]. Recently, the advances in inference and learning algorithms for various deep generative models significantly enhanced this line of research [2,7,8,18].

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