Few-Shot Learning (FSL) is a technique aimed at improving a model’s ability to generalize to unseen categories using only a small amount of labeled data. Representative shift vectors and the hubness problem are two common issues that often hinder the performance of FSL. Representative shift vectors refer to the inherent bias caused by the absolute positional differences of various base classes in the feature space. The hubness problem occurs when a class prototype becomes the nearest neighbor for many test instances, regardless of their true class. Inspired by generative models, we find that these two problems can be effectively addressed simultaneously. By leveraging generative models, we eliminate representative shift vectors through learning intra-class diversity and enhance the quality of generated samples by utilizing hubness samples. Experimental results demonstrate that our method improves performance, and our code will be available at https://github.com/cql33/HG-VAE.