Depth feature representation method based on multiple stacked auto-encoding

TL;DR: In this paper, a depth feature representation method based on multiple stacked auto-encoding networks of different structures is proposed, where a shallow-layer neural network structure is constructed, a back-propagation method is adopted to train network parameters to enable a neural network to achieve the optimal structure, and outputs, namely the feature expressions, of a second layer of the network are acquired.

Abstract: The invention relates to a depth feature representation method based on multiple stacked auto-encoding. Feature expressions of different hierarchical structures of a target object are acquired through constructing stacked auto-encoding networks of different structures. A shallow-layer (the number of hidden layers is smaller) neural network structure is constructed, a back-propagation method is adopted to train network parameters to enable a neural network to achieve the optimal structure, and outputs, namely the feature expressions, of a second layer of the network are acquired; more deeply hierarchical network structures are respectively established, network parameters are trained according to a similar manner, and thus outputs (the feature expressions) of corresponding layers are acquired; and fusion and selection are carried out on the above-mentioned obtained features according to a manner of feature combination and selection to acquire a hierarchical feature representation that characterizes a target. Therefore, corresponding visual tasks (image classification, identification and detection) are carried out.