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Super-network training method and device, electronic equipment and storage medium

A training method and a training device technology, applied in the field of deep learning, can solve problems such as limiting the representation ability of neural networks, and achieve the effect of improving the representation ability

Pending Publication Date: 2020-04-03
BEIJING XIAOMI INTELLIGENT TECH CO LTD
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Problems solved by technology

[0003] In the above neural network architecture search scheme, in the single-path neural sub-network trained for each sampling, in order to ensure the fairness of the sampled sub-modules, only one sub-structure can be sampled in each layer, but this will limit the representation ability of the neural network

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  • Super-network training method and device, electronic equipment and storage medium
  • Super-network training method and device, electronic equipment and storage medium
  • Super-network training method and device, electronic equipment and storage medium

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[0065] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of devices consistent with aspects of the present disclosure as recited in the appended claims.

[0066] At present, in the existing neural network architecture search (NAS), a single-path neural sub-network is usually used to implement, that is, only one sub-module is sampled from each layer of the super-network, and the sampled sub-modules are sequentially connected in series to form a single-path neural sub-network. path neural sub-network; then, share the parameters of each layer of sub-module...

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Abstract

The invention relates to a super-network training method and device, electronic equipment and a storage medium. The super-network training method comprises the steps: obtaining a multi-path neural sub-network, and carrying out the training of the multi-path neural sub-network, so as to update the weight parameters of all substructures; synchronizing the weight parameter of each substructure in themultipath neural sub-network to the initial super-network; and ending the training and obtaining a target super network when the super network converges. Thus, under the condition that the characterization capability of a single path is limited, the multi-path neural sub-network is utilized to train the super-network, and the characterization capability of the super-network can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of deep learning, and in particular to a hypernetwork training method and device, electronic equipment, and storage media. Background technique [0002] At present, in the existing neural network architecture search (NAS), a single-path neural sub-network is usually used to implement, that is, only one sub-module is sampled from each layer of the super-network, and the sampled sub-modules are sequentially connected in series to form a single-path neural sub-network. path neural sub-network; then, share the parameters of each layer of sub-modules from the super-network; after that, perform single-step training on this single-path neural sub-network. After the training of the single-path neural sub-network is completed, the parameters of each sub-module are shared with the super-network. In this way, the steps of "sampling-sharing parameters-single-step training-updating parameters" are repeated unti...

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/08G06N3/047G06N3/045G06N3/082G06N3/049G06N3/04
Inventor 初祥祥张勃许瑞军王斌
Owner BEIJING XIAOMI INTELLIGENT TECH CO LTD
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