Small sample learning method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a small sample learning method and device, electronic equipment and a storage medium. The small sample learning method comprises the following steps: encoding an image training sample set according to an image representation model to obtain an image matrix formed by image vector representation of each image training sample; encoding labels of the image training sample set according to a label preprocessing model to obtain a label matrix formed by label vector representation of each image training sample label; and performing back propagation according to the loss values of the image matrix and the label matrix to perform parameter optimization on the image representation model and the label preprocessing model so as to obtain a trained image representation model and a trained label preprocessing model. According to the invention, knowledge in a natural language task is introduced into a feature recognition task of an image, fusion of different task knowledge is realized, learning of image features under the condition of a small sample data set is accelerated, and efficiency and accuracy of image feature learning under the condition of the small sample data set are improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of machine learning, and in particular, to a small-sample learning method, device, electronic equipment, and storage medium. Background technique [0002] Because the conventional deep learning algorithm needs a large number of training sample sets to achieve better results, it limits its application in many fields. [0003] At present, for the feature learning of small-sample training sets, commonly used technical methods include data enhancement, transfer learning, meta-learning, etc., but these methods still have shortcomings such as poor performance in cross-domain tasks and catastrophic forgetting. The effect of deep learning under the training set is not ideal. Contents of the invention [0004] Embodiments of the present invention provide a small-sample learning method, device, electronic device, and storage medium, so as to improve the learning effect in the case of small...

AI Technical Summary

Problems solved by technology

[0002] Because the conventional deep learning algorithm needs a large number of training sample sets to achieve better results, it limits its application in many fields.

[0003] At present, for the feature learning of small-sample training sets, commonly used technical methods include data enhancement, transfer learning, meta-learning, etc., but these methods still have shortcomings such as poor performance in cross-domain tasks and catastrophic forgetting. The effect of deep learning under the training set is not ideal

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