Generative Deep Deconvolutional Learning

Yunchen Pu, Xin Yuan, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

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A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up and top-down probabilistic learning. After learning the deep convolutional dictionary, testing is implemented via deconvolutional inference. To speed up this inference, a new statistical approach is proposed to project the top-layer dictionary elements to the data level. Following this, only one layer of deconvolution is required during testing. Experimental results demonstrate powerful capabilities of the model to learn multi-layer features from images. Excellent classification results are obtained on both the MNIST and Caltech 101 datasets.
Original languageEnglish (US)
JournalArxiv preprint
StatePublished - Dec 18 2014
Externally publishedYes


  • stat.ML
  • cs.LG


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