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DBN proposed by Hinton et al. [43] is a graphical model that uses probabilities and unsupervised learning to extract a deep hierarchical representation of the training data.
Its network is like a stack of Restricted Boltzmann Machines (RBMs) in the middle, and the last layer is a classifier. The nodes in each layer of a DBN are connected to all the nodes in the previous and subsequent layer.
Different from CNN, DBN works globally and each layer will learn the entire input and is regulated in order. It makes the optimal choice at each layer in the sequence, eventually finding a global optimum |
IEEE/ICACT20210175 Slide.23
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