  We study object recognition under the constraint that each object class is
only represented by very few observations. Semi-supervised learning, transfer
learning, and few-shot recognition all concern with achieving fast
generalization with few labeled data. In this paper, we propose a generic
framework that utilizes unlabeled data to aid generalization for all three
tasks. Our approach is to create much more training data through label
propagation from the few labeled examples to a vast collection of unannotated
images. The main contribution of the paper is that we show such a label
propagation scheme can be highly effective when the similarity metric used for
propagation is transferred from other related domains. We test various
combinations of supervised and unsupervised metric learning methods with
various label propagation algorithms. We find that our framework is very
generic without being sensitive to any specific techniques. By taking advantage
of unlabeled data in this way, we achieve significant improvements on all three
tasks.
