Small sample learning method based on multi-scale metric learning

Abstract

The invention discloses a small sample learning method based on multi-scale metric learning. The method comprises the following steps: 1, establishing a data set; 2, generating a multi-scale feature mapping layer; 3, performing transfer learning: performing secondary mapping on the multi-scale features of the sample by a conversion module; 4, generating a multi-scale feature mapping pair; 5, calculating a relationship score of the multi-scale feature mapping pair in the multi-scale relationship generation network; and 6, measuring the sample similarity by adopting a multi-scale measurement learning model. The method is simple in structure and reasonable in design, the multi-scale feature mapping pairs are obtained through transfer learning, the trained model has mobility, loss items brought to the whole model by sample spacing are added on the basis of a mean square error loss function to form a new loss function, metric learning is achieved, and the method adapts to training of smallsample learning.

Description

technical field [0001] The invention belongs to the technical field of image processing and recognition, and in particular relates to a small-sample learning method based on multi-scale metric learning. Background technique [0002] Humans are very good at recognizing a new object with a very small number of samples. For example, children only need some pictures in the book to know what is a "zebra" and what is a "rhinoceros". Inspired by the rapid learning ability of human beings, researchers hope that after learning a large amount of data of a certain category, the machine learning model can quickly learn new categories with only a small number of samples. This is what Few-shot Learning is to solve question. [0003] For machine learning, although deep learning in image recognition tasks can achieve very satisfactory results in some scenarios with deep and complex network models, huge training data support, and powerful hardware support, but in In some rare task scenario...

AI Technical Summary

Problems solved by technology

However, the common algorithm of transfer learning cannot solve the small-sample learning task very well. The main difference is that the small-sample learning needs to acquire the ability to identify unknown categories, which means that in principle it has the ability to identify a large number of untrained category target, and obviously transfer learning cannot do this

Images