# Learning Structured Output Representation using Deep Conditional Generative Models

@inproceedings{Sohn2015LearningSO, title={Learning Structured Output Representation using Deep Conditional Generative Models}, author={Kihyuk Sohn and Honglak Lee and Xinchen Yan}, booktitle={NIPS}, year={2015} }

Supervised deep learning has been successfully applied to many recognition problems. [... ] Key Method The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows for fast prediction using stochastic feed-forward inference. In addition, we provide novel strategies to build robust structured prediction algorithms, such as input noise-injection and multi-scale prediction objective at training. In experiments, we demonstrate the effectiveness of our proposed algorithm in… Expand

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