Method and device for training a multi-label classification model

A classification model, multi-label technology, applied in the computer field, can solve the problem of not being able to input images, and achieve good results

Pending Publication Date: 2019-06-04
HUAWEI TECH CO LTD +1
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Problems solved by technology

After the features of the image are extracted, the features of the image are fixed, so it is not

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  • Method and device for training a multi-label classification model
  • Method and device for training a multi-label classification model
  • Method and device for training a multi-label classification model

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Embodiment Construction

[0055] The technical solution in this application will be described below with reference to the accompanying drawings.

[0056] figure 1 Schematic diagrams showing single-label classification and multi-label classification problems. like figure 1 As shown in (a), single-label classification often assumes that samples only correspond to one category label, that is, have unique semantic meaning. However, this assumption may not hold true in many practical situations, especially considering the semantic diversity of the objective object itself, the object is likely to be associated with multiple different category labels at the same time. Therefore, in multi-label problems, such as figure 1 As shown in (b), multiple related category labels are often used to describe the semantic information corresponding to each object. For example, each image may correspond to multiple semantic labels at the same time, such as "grassland", "sky" and "sea". ", each piece of music may contain ...

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Abstract

The invention provides a method and a device for training a multi-label classification model, which can dynamically learn image features, enable a feature extraction network to better adapt to task requirements, and are good in multi-label classification effect. The method comprises the steps of determining n samples and a label matrix Yc * n corresponding to the n samplesfrom a training data set,wherein an element yi * j in the label matrix Yc * n represents whether an ith sample contains an object indicated by a jth label or not, and c represents the number of labels related to the samples;extracting a feature matrix Xd * n of the n samples by using a feature extraction network; using a first mapping network to obtain a prediction label matrix of the characteristic matrix Xd * n, and using a second mapping network to obtain a low-rank label matrix of the label matrix, wherein the prediction tag matrix and the low-rank tag matrix are defined in the specification; updating the weightparameter Z, the feature mapping matrix Mc * d and the low-rank label correlation matrix S, and training the multi-label classification model.

Description

technical field [0001] The present application relates to the computer field, and more specifically, to a method and device for training a multi-label classification model in the computer field. Background technique [0002] With the improvement of the processing performance of smart phones, more and more applications have put forward requirements for image recognition. For example, in the process of taking pictures with a mobile phone, if the smartphone can accurately identify objects within the shooting range, it can perform targeted calculations on its color and shape, thereby improving the shooting effect. In the machine learning of intelligent systems, the training of recognizing objects in images has become a very important aspect. Generally speaking, machine learning is to set labels for a large number of existing images for the objects contained in them, and then continuously adjust the recognition parameters through computer self-evolution to gradually improve the ...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/00
Inventor 刘晓阳胡晓林王月红曹忆南
Owner HUAWEI TECH CO LTD
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