Few-Shot Learning for Images Classification Considering Feature Variance

Few-shot learning aims to solve the problem of learning with limited samples. However, existing metric-based meta-learning models have the issue of insufficient attention to feature representation capability. Therefore, this paper introduces the feature variance loss during network training to learn more powerful features. Experimental results demonstrate that our method achieves good learning performance on the miniImageNet and tieredImageNet datasets.