Max-margin Deep Generative Models

@inproceedings{Li2015MaxmarginDG,
  title={Max-margin Deep Generative Models},
  author={Chongxuan Li and Jun Zhu and Tianlin Shi and Bo Zhang},
  booktitle={NIPS},
  year={2015}
}
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, little work has been done on examining whether the representations are discriminative enough to get good prediction performance. In this paper, we present max-margin deep generative models (mmDGMs), which explore the strongly discriminative principle of max-margin learning to improve the discriminative power of… CONTINUE READING
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