Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning. ICLR'19

• Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning. ICLR'19
  • Abstract
  • 3 Main Approach
    • 3.1 Problem Definition

Abstract

• 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.
• we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data. TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner.

3 Main Approach

3.1 Problem Definition

• We follow the episodic paradigm (from MatchingNet) that effectively trains a meta-learner for fewshot classification tasks, which is commonly employed in various literature, like: ProtoNet, MAML, Reptile, RelationNet, SNAIL

tags: fewshot learning