  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.
