A generative model for deep convolutional learning

Published

Conference Paper

© 2015 International Conference on Learning Representations, ICLR. All rights reserved. A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. Experimental results demonstrate powerful capabilities of the model to learn multi-layer features from images, and excellent classification results are obtained on the MNIST and Caltech 101 datasets.

Duke Authors

Cited Authors

  • Pu, Y; Yuan, X; Carin, L

Published Date

  • January 1, 2015

Published In

  • 3rd International Conference on Learning Representations, Iclr 2015 Workshop Track Proceedings

Citation Source

  • Scopus