Max-Margin Deep Generative Models for (Semi-)Supervised Learning

@article{Li2018MaxMarginDG,
  title={Max-Margin Deep Generative Models for (Semi-)Supervised Learning},
  author={Chongxuan Li and Jun Zhu and Bo Zhang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2018},
  volume={40},
  pages={2762-2775}
}
Deep generative models (DGMs) can effectively capture the underlying distributions of complex data by learning multilayered representations and performing inference. However, it is relatively insufficient to boost the discriminative ability of DGMs. This paper presents max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs), which explore the strongly discriminative principle of max-margin learning to improve the predictive performance of DGMs in both supervised and… CONTINUE READING
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