  Few-shot learning amounts to learning representations and acquiring knowledge
such that novel tasks may be solved with both supervision and data being
limited. Improved performance is possible by transductive inference, where the
entire test set is available concurrently, and semi-supervised learning, where
more unlabeled data is available. Focusing on these two settings, we introduce
a new algorithm that leverages the manifold structure of the labeled and
unlabeled data distribution to predict pseudo-labels, while balancing over
classes and using the loss value distribution of a limited-capacity classifier
to select the cleanest labels, iteratively improving the quality of
pseudo-labels. Our solution surpasses or matches the state of the art results
on four benchmark datasets, namely miniImageNet, tieredImageNet, CUB and
CIFAR-FS, while being robust over feature space pre-processing and the quantity
of available data. The publicly available source code can be found in
https://github.com/MichalisLazarou/iLPC.
