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

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 classificati...

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Bibliographic Details
Published in:arXiv.org 2019-02
Main Authors: Liu, Yanbin, Lee, Juho, Park, Minseop, Kim, Saehoon, Yang, Eunho, Hwang, Sung Ju, Yang, Yi
Format: Article
Language:English
Subjects:
Classification
Classifiers
Labels
Learning
Propagation
State of the art
Online Access:Get full text
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Summary: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. Specifically, 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. We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.
ISSN:2331-8422