DDNN training method and DDNN-based multi-view target recognition method and system

Abstract

The invention discloses a DDNN training method and a DDNN-based multi-view target recognition method and system, and belongs to the field of cloud computing. The method comprises the steps of obtaining an information entropy of a distributed deep neural network cloud side model to a sample image; constructing a DDNN target function based on the information entropy of the sample image; and according to the DDNN target function, jointly training an edge side model and a cloud side model of the DDNN. In the knowledge migration method focused on a teacher-student network, under the background of multiple outlets of a DDNN level, the adaptive training method based on sample weighting is provided, scores of samples are obtained from a deep outlet of a DDNN, the samples are weighted through the scores to distinguish simple and complex samples, the weighted samples are used for training a cloud side model and an edge side model at the same time, and the communication traffic is minimum while good classification precision is guaranteed. The cloud side model guides the whole training process of the edge model, and the edge model can learn real labels and cloud side migration knowledge at thesame time.

Description

technical field [0001] The invention belongs to the field of cloud computing, and more specifically, relates to a DDNN training method and a DDNN-based multi-view target recognition method and system. Background technique [0002] Deep Neural Network (DNN) has a multi-layer structure, and its expression learning is also distributed in layers. For the input vector, the layer-by-layer transmission will bring delay to the layer behind the DNN, and as the operation parameters continue to accumulate, the calculation Energy consumption also increases layer by layer, which is not conducive to the real-time control of radio resources in the next generation mobile network. On this basis, a distributed deep neural network (Distributed Deep Neural Network, DDNN) model is proposed, which has a distributed computing hierarchy. DDNN for edge computing refers to mapping a single DNN part to a distributed heterogeneous device. Including cloud, edge and geographically distributed end device...

AI Technical Summary

Problems solved by technology

[0004] However, the complexity of different sample images is also different, it is difficult to define a model directly corresponding to it to select the appropriate sample image

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