Attention mechanism relationship comparison network model method based on small sample learning

Abstract

The invention discloses an attention mechanism relationship comparison network model method. An attention relationship comparison network model for small sample learning under a small amount of labeled sample data is constructed. Based on a relational network architecture, the model is divided into a feature coding part, a feature combination part and a relational coding part, the feature coding module is used for extracting image feature information, and the feature combination part is used for recombining the extracted query image feature information with the training image feature information of each group to form a new combined feature map. The relation encoding module performs nonlinear metric learning of the network; by introducing an attention mechanism and a spectrum normalizationmethod into an end-to-end deep convolutional neural network model, the model has higher classification accuracy under the condition of small sample learning, the stability of a final training result of the model is improved, and the image classification accuracy of an existing model in small sample learning is improved.

Description

technical field [0001] The invention belongs to the technical fields of image processing, pattern recognition and machine vision, and relates to image classification and recognition network model technology, in particular to a network model method based on small sample learning attention mechanism relation comparison, which is constructed on a small amount of labeled sample data for small Compared with the network model, the attention relationship of sample learning can effectively improve the accuracy of image classification under small sample learning. Background technique [0002] In recent years, the unprecedented breakthroughs of deep learning in various fields have largely relied on the large amount of available labeled data, which requires a lot of cost to collect and annotate, which severely limits the ability to learn new categories. More importantly, these deep learning models are difficult to solve the problem of a small amount of labeled data. Therefore, the use...

AI Technical Summary

Problems solved by technology

Using deep convolutional networks to extract image features is a critical step in the process of small-sample learning. However, using existing meta-learning methods for small-sample learning tasks makes it difficult for deep convolutional networks to improve the classification accuracy of the model. And can stabilize the final training result of the model

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