Research on image classification method based on improved multi-scale relational network.

ABSTRACT: Small sample learning aims to learn information about object categories from a single or a few training samples. This learning style is crucial for deep learning methods based on large amounts of data. The deep learning method can solve small sample learning through the idea of meta-learning "how to learn by using previous experience." Therefore, this paper takes image classification as the research object to study how meta-learning quickly learns from a small number of sample images. The main contents are as follows: After considering the distribution difference of data sets on the generalization performance of measurement learning and the advantages of optimizing the initial characterization method, this paper adds the model-independent meta-learning algorithm and designs a multi-scale m

SUBMITTER: Zheng W 

PROVIDER: S-EPMC8323718 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature