Deep network migration learning method facing a marked noise apparent age database

A technology of transfer learning and deep network, which is applied in the field of marked-noise apparent age databases, can solve problems such as data noise, low precision, and result errors, and achieve the effects of improving recognition accuracy, weakening influence, and increasing reliability

Active Publication Date: 2018-12-11
奕通信息科技(上海)股份有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

If the apparent age database containing label noise is not processed to a certain extent, then if such an age database is used directly, the obtained results will contain certain errors and the accuracy will be low
[0003] There are many mature data processing technologies in the database field. However, for the above-mentioned problem of data n...

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  • Deep network migration learning method facing a marked noise apparent age database
  • Deep network migration learning method facing a marked noise apparent age database
  • Deep network migration learning method facing a marked noise apparent age database

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Embodiment Construction

[0040] The present invention discloses a deep network migration learning method oriented to a labeled noise apparent age database. The specific implementation of the present invention will be further described below in combination with preferred embodiments.

[0041] The simulation experiment carried out by the present invention is programmed on a PC test platform with a CPU of 3.6GHz and a memory of 15.5G.

[0042] Such as figure 1 As shown, the present invention is a deep network transfer learning method for a labeled noise apparent age database, and the specific steps are as follows:

[0043] (1), the apparent age database is randomly divided into two parts according to the preset ratio, one part is a training set, and the other part is a verification set, and the proportion of the training set is greater than that of the verification set;

[0044] (2) A small amount of data is randomly selected from the training set, and repeated n times to obtain n sub-training sets, whi...

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Abstract

The invention discloses a deep network migration learning method facing a marked noise apparent age database. The apparent age database is randomly divided into two parts according to the preset proportion, one is training set, and the other is verification set. The proportion of training set is higher than verification set. A small amount of data is randomly extracted from the training set and repeated n times to obtain n sub-training sets. The set of the remaining data in the training set is denoted as data set A. According to the transfer learning method, n classification models are obtained by in-depth learning of n sub-training sets, and then the data set A is identified by using n classification models. The deep network migration learning method facing the marked noise apparent age database enables the apparent age database with high accuracy to be obtained when the marked noise is weakened by the apparent age database, and can effectively weaken the influence of the marked noiseon the experimental result and make the result more credible.

Description

technical field [0001] The invention relates to a method for a marked noise apparent age database, in particular to a deep network migration learning method for a marked noise apparent age database. Background technique [0002] A person's biological age is calculated from the moment of birth, and one year is added to the biological age every time one year passes, which shows that the biological age will not be changed by the influence of the external environment. However, in the apparent age database, these apparent ages are related to a person's cultivation, responsibility, experience, and psychology, and each person's performance in these aspects is also different, so when a person's age When judging by appearance, there will be a gap with the biological age. Here, the database obtained by marking the age of a person according to the appearance of the human eye is called an apparent age database. In the process of labeling the apparent age data, one person may be used f...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/178G06V40/172G06F18/24G06F18/214
Inventor 王结太
Owner 奕通信息科技(上海)股份有限公司
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