Depth measurement model optimization method and device thereof, and storage medium

An optimization method and model technology, applied in the field of deep learning, can solve the problems that it is difficult to improve the discrimination ability of the depth measurement model, and achieve the effect of improving the recognition ability

Pending Publication Date: 2021-10-19
ALIBABA GRP HLDG LTD
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Problems solved by technology

[0003] Existing metric learning methods are not easy to improv

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  • Depth measurement model optimization method and device thereof, and storage medium
  • Depth measurement model optimization method and device thereof, and storage medium
  • Depth measurement model optimization method and device thereof, and storage medium

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[0022] In order to make the objects, technical solutions and advantages of the present application, the technical solutions of the present application will be described in conjunction with the specific embodiments and corresponding drawings of the present application. Obviously, the described embodiments are merely the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, one of ordinary skill in the art is in the scope of the present application without making creative labor premistence.

[0023] Depth metric learning, mainly for training data, independent learning, data-based feature, to calculate data, so that data with similar features is as close as possible, with different types of features as far as possible. Depth metrics learn more for deep learning retrieval tasks and classified tasks, and play an important role. For example, in a classified task, a similarity between two images can be calculated based on...

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Abstract

The invention provides a depth measurement model optimization method and device and a storage medium. When the depth measurement model is trained, the graph models output by the depth measurement model for different sample data sets are obtained, the graph consistency loss can be obtained based on the features shown by the different graph models in the structural aspect, and the graph consistency loss can effectively represent the stability of the depth measurement model in the feature identification aspect. The depth measurement model is trained based on the graph consistency loss, the identification capability of the depth measurement model for different features and the same features can be improved in a targeted manner, and the performance of the depth measurement model in various tasks such as classification and query is improved.

Description

technical field [0001] The present application relates to the technical field of deep learning, and in particular to an optimization method, device and storage medium of a deep metric model. Background technique [0002] In the field of deep learning, deep metric learning (Deep Metric Learning, DML) has a wide range of applications. For example, in a retrieval task based on deep learning, DML can be used to learn how to make similar features as close as possible and different features as far away as possible to improve the reliability of retrieval results. [0003] Existing metric learning methods are not easy to improve the discriminative ability of deep metric models for features. Therefore, a new solution remains to be proposed. Contents of the invention [0004] Various aspects of the present application provide an optimization method, device, and storage medium for a depth metric model, so as to improve the feature discrimination capability of the depth metric model...

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Application Information

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IPC IPC(8): G06K9/62G06N20/20
CPCG06N20/20G06F18/22G06F18/214
Inventor 陈炳辉
Owner ALIBABA GRP HLDG LTD
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