Inference method and system for dynamic branch selection of deep learning model

A technology of deep learning and reasoning methods, applied in the field of artificial intelligence deep learning reasoning, can solve problems such as feature redundancy and insufficient reasoning speed of edge computing equipment, and achieve the effect of improving reasoning speed and reducing computing redundancy

Active Publication Date: 2021-03-05
魔视智能科技(上海)有限公司
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AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the feature redundancy of the current deep learning model leads to insufficient reasoning speed in edge computing devices, the present invention provides a reasoning method and system for dynamic branch selection of deep learning models with low redundancy and high speed, which are suitable for edge computing devices

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  • Inference method and system for dynamic branch selection of deep learning model
  • Inference method and system for dynamic branch selection of deep learning model
  • Inference method and system for dynamic branch selection of deep learning model

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Embodiment Construction

[0019] Such as figure 1 as well as figure 2 As shown, the embodiment of this specification provides a reasoning method for dynamic branch selection of a deep learning model, including:

[0020] S101. Construct and train a deep learning model for semantic classification, semantic detection or semantic segmentation of pictures. The deep learning model includes an encoder 11, a branch selector 12, and a plurality of decoding branches 13 corresponding to different branch categories one by one, The deep learning model is trained as:

[0021] A plurality of primary features (intermediate features) are extracted from the input picture by the encoder 11 .

[0022] The branch classes of the primary features are identified and output by the branch selector 12 .

[0023] The decoding branch 13 processes all the primary features through the feature weight group corresponding to the branch category to obtain deep features, recognizes the deep features and outputs the inference result. ...

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Abstract

The invention provides an inference method and system for dynamic branch selection of a deep learning model, and aims to solve the problem of redundancy of deep features of a convolutional neural network in a decoding part in the inference process of a traditional deep learning model, and can automatically select corresponding decoding branches according to the category of primary features, each decoding branch is trained through the primary feature input and the corresponding output of the corresponding branch category to obtain different feature weight sets, and the method can focus on the processing of the primary features of the corresponding branch category and the recognition of deep features to complete more complex classification or representation generation, therefore, the computational redundancy is reduced, the reasoning speed is increased, and the actual application requirements of edge computing equipment are met.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence deep learning reasoning, and in particular relates to a reasoning method and system for dynamic branch selection of a deep learning model applied on an edge computing device. Background technique [0002] The current edge computing terminal applications have more and more demands for deep learning vision algorithms, but due to the cost of current edge terminals, the running inference delay of high-precision and complex models cannot meet the practical application, and the accuracy of simple models cannot meet the requirements. This is because For data-intensive and complex tasks, complex convolutional neural network models are usually required to fit the tasks well, and models that are too lightweight will underfit. [0003] In addition, during the inference process of deep learning visual models, there will be redundancy in the deep features generated by the convolutional neural n...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06N5/04
CPCG06N3/08G06N5/04G06N3/045G06F18/24G06F18/214
Inventor 李发成袁施薇张如高虞正华
Owner 魔视智能科技(上海)有限公司
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