Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2019

Abstract

Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning. We additionally learn a soft weighting network (SWN) to optimize the self-training weights of pseudo labels so that better ones can contribute more to gradient descent optimization. We evaluate our LST method on two ImageNet benchmarks for semi-supervised few-shot classification and achieve large improvements over the state-of-the-art.

Keywords

Few-shot learning, semi-supervised learning, meta-learning, image classification

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Advances in Neural Information Processing Systems: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, December 8

First Page

1

Last Page

11

Publisher

NIPS

City or Country

La Jolla, CA

Copyright Owner and License

Authors

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