Poster

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

Xiu-Shen Wei · H.-Y. Xu · Faen Zhang · Yuxin Peng · Wei Zhou

Hall J #913

Keywords: [ Few-Shot Learning ] [ Semi-Supervised Few-Shot Learning ] [ Negative Learning ]

[ Abstract ]
[ Poster [ OpenReview
Thu 1 Dec 9 a.m. PST — 11 a.m. PST
 
Spotlight presentation: Lightning Talks 6A-4
Thu 8 Dec 6:30 p.m. PST — 6:45 p.m. PST

Abstract:

Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Many sophisticated methods have been developed to address the challenges this problem comprises. In this paper, we propose a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective, and then augment the extremely label-constrained support set in few-shot classification tasks. Our approach can be implemented in just few lines of code by only using off-the-shelf operations, yet it is able to outperform state-of-the-art methods on four benchmark datasets.

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