All you need is a good representation: A multi-level and classifier-centric representation for few-shot learning

This work proposes a classifier-centric loss for learning the representation of each level, which forces samples to be centered on their respective classifier weights in the feature space, and achieves a significant improvement over baseline method in cross-task validation, and demonstrates its superiority in alleviating the domain shift problem.