Balancing Feature Alignment and Uniformity for Few-Shot Classification

Balancing Feature Alignment and Uniformity for Few-Shot Classification

Information Science, Artificial Intelligence,
few-shot learning   mutual information   feature representation   knowledge distillation   self-supervised augmentation  

Solving Few-Shot Classification Problems with Balanced Feature Alignment and Uniformity Background and Motivation The goal of Few-Shot Learning (FSL) is to correctly recognize new samples with only a few examples from new classes. Existing few-shot learning methods mainly learn transferable knowledge from base classes by maximizing the information ...