A generative model for deep convolutional learning
Publication
, Conference
Pu, Y; Yuan, X; Carin, L
Published in: 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings
January 1, 2015
© 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 Scholars
Published In
3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings
Publication Date
January 1, 2015
Citation
APA
Chicago
ICMJE
MLA
NLM
Pu, Y., Yuan, X., & Carin, L. (2015). A generative model for deep convolutional learning. In 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings.
Pu, Y., X. Yuan, and L. Carin. “A generative model for deep convolutional learning.” In 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings, 2015.
Pu Y, Yuan X, Carin L. A generative model for deep convolutional learning. In: 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings. 2015.
Pu, Y., et al. “A generative model for deep convolutional learning.” 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings, 2015.
Pu Y, Yuan X, Carin L. A generative model for deep convolutional learning. 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings. 2015.
Published In
3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings
Publication Date
January 1, 2015