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A generative model for deep convolutional learning
Published: Jan 1, 2015
Abstract
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...
Paper Details
Title
A generative model for deep convolutional learning
Published Date
Jan 1, 2015
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