Abstract
Few-shot classification is challenging because the data distribution of the training set can be widely different to the distribution of the test set as their classes are disjoint. This distribution shift often results in poor generalization. Manifold smoothing has been shown to address the distribution shift problem by extending the decision boundaries and reducing the noise of the class representations. Moreover, manifold smoothness is a key factor for semi-supervised learning and transductive learning algorithms. In this work, we present embedding propagation as an unsupervised non-parametric regularizer for manifold smoothing. Embedding propagation leverages interpolations between the extracted features of a neural network based on a similarity graph. We empirically show that embedding propagation yields a smoother embedding manifold. We also show that incorporating embedding propagation to a transductive classifier leads to new state-of-the-art results in mini-Imagenet, tiered-Imagenet, and CUB. Furthermore, we show that embedding propagation results in additional improvement in performance for semi-supervised learning scenarios.