基于样本对元学习的小样本图像分类方法

作者：Li Wei-Gang; Gan Ping^*; Xie Lu; Li Song-Tao

来源：Acta Electronica Sinica, 2022, 50(2): 295-304.

DOI：10.12263/DZXB.20210453

• Few-shot image
• Meta learning
• Meta loss function
• Transitive transfer learning

In this paper, a pairwise-based meta learning(PML) method is proposed for few-shot image classification. Transitive transfer learning is used to fine tune the pre-trained Resnet50 model to get a feature encoder that is more suitable for few shot task. The feature encoder is used as the initial feature encoder of the meta-learning model to train the model, which further enhances the generalization ability of the meta-learning model. Based on the similarity between the support set and the query set samples, a meta loss(ML) function is proposed, which considers the relationship between all the samples of the query set in the feature space, so as to reduce the within-class distance of positive samples and increase the between-class distance of positive and negative samples, thus improving the classification accuracy.The experimental results show that the classification accuracy of the methods in this paper is 77.65% and 89.65% on 1-shot and 5-shot tasks, respectively, and it is 7.38% and 5.65% higher than the latest meta-learning method, Meta-baseline. ? 2022, Chinese Institute of Electronics. All right reserved.

• 单位
  武汉科技大学