Deep belief networks

Deep Belief Networks

The important aspect of this layer-wise training procedure is that, provided the number of features per layer does not decrease, [6] showed that each extra layer increases a variational lower bound on the log probability of data. So layer-by-layer training can be repeated several times1 to learn a deep, hierarchical model in which each layer of features captures strong high-order correlations b...

Deep belief networks

Multiresolution Deep Belief Networks

Motivated by the observation that coarse and fine resolutions of an image reveal different structures in the underlying visual phenomenon, we present a model based on the Deep Belief Network (DBN) which learns features from the multiscale representation of images. A Laplacian Pyramid is first constructed for each image. DBNs are then trained separately at each level of the pyramid. Finally, a t...

Studies in Deep Belief Networks

Deep networks are able to learn good representations of unlabelled data via a greedy layer-wise approach to training. One challenge arises in choosing the layer types to use, whether it is an autoencoder, restricted boltzmann machine, with and without sparsity regularization. The layer choice directly affects the type of representations learned. In this paper, we examine sparse autoencoders and...

Breast cancer classification using deep belief networks

Over the last decade, the ever increasing world-wide demand for early detection of breast cancer at many screening sites and hospitals has resulted in the need of new research avenues. According to theWorld Health Organization (WHO), an early detection of cancer greatly increases the chances of taking the right decision on a successful treatment plan. The Computer-Aided Diagnosis (CAD) systems ...