| CPC G06V 10/764 (2022.01) [G06N 3/08 (2013.01); G06V 10/72 (2022.01); G06V 10/82 (2022.01)] | 18 Claims |
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1. A training method for an image identifying model, comprising:
obtaining image samples of a plurality of categories;
inputting image samples of each category of the plurality of categories into a feature extraction layer of the image identifying model to extract a feature vector of each image sample;
calculating a statistical characteristic information of an actual distribution function corresponding to each category according to the feature vector of each image sample of the each category;
establishing an augmented distribution function corresponding to the each category according to the statistical characteristic information of the actual distribution function corresponding to the each category;
obtaining augmented sample features of the each category based on the augmented distribution function corresponding to the each category; and
inputting feature vectors of the image samples and the augmented sample features into a classification layer of the image identifying model for supervised learning,
wherein the statistical characteristic information comprises a first statistical characteristic information and a second statistical characteristic information; and
the establishing of the augmented distribution function corresponding to the each category comprises:
calculating an average value of the second statistical characteristic information of actual distribution functions corresponding to the plurality of categories; and
establishing the augmented distribution function corresponding to the each category in a case where the first statistical characteristic information of the each category and the average value of the second statistical characteristic information are used as statistical characteristic parameters.
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