Distribution Estimation Based Pseudo-Feature Library Generation For Few-Shot Image Classification

Due to the high cost of labeled data acquisition, few-shot learning has attracted great attention in recent years. The biased estimation of class distribution from a few labeled samples hinders the model’s performance. Some existing methods generate samples or features by a learning module or network. In this paper, a distribution-based pseudo-feature library generation method is proposed, and it ...