computer science Computer science is the study of computation, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, ...

, a convolutional deep belief network (CDBN) is a type of

deep Deep or The Deep may refer to: Places United States * Deep Creek (Appomattox River tributary), Virginia * Deep Creek (Great Salt Lake), Idaho and Utah * Deep Creek (Mahantango Creek tributary), Pennsylvania * Deep Creek (Mojave River tributary ...

artificial neural network In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure and functions of biological neural networks. A neural network consists of connected ...

composed of multiple layers of convolutional

restricted Boltzmann machine A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a prob ...

s stacked together. Alternatively, it is a hierarchical

generative model In statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsiste ...

for deep learning, which is highly effective in

image processing An image or picture is a visual representation. An image can be two-dimensional, such as a drawing, painting, or photograph, or three-dimensional, such as a carving or sculpture. Images may be displayed through other media, including a pr ...

and

object recognition Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the ...

, though it has been used in other domains too. The salient features of the model include the fact that it scales well to high-dimensional images and is translation-invariant. CDBNs use the technique of probabilistic max-pooling to reduce the dimensions in higher layers in the network. Training of the network involves a pre-training stage accomplished in a greedy layer-wise manner, similar to other

deep belief network In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not b ...

s. Depending on whether the network is to be used for discrimination or generative tasks, it is then "fine tuned" or trained with either

back-propagation In machine learning, backpropagation is a gradient computation method commonly used for training a Neural network (machine learning), neural network to compute its parameter updates. It is an efficient application of the chain rule to neural ne ...

or the up–down algorithm (contrastive–divergence), respectively.

References

Artificial neural networks Probabilistic models {{probability-stub