Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

Understand

The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.

• The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task.
• Yet, even with such meta-learning, the low-data problem in the novel classification task still remains.
• In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.