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. Although it can approximate a complex many-to-one function well when a large amount of training data is provided, it is still challenging to model complex structured output representations that effectively perform probabilistic inference and make diverse predictions. In this work, we develop a deep conditional generative model for structured output prediction using Gaussian latent variables. The model is… CONTINUE READING
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