Neighbor Matching for Semi-supervised Learning

Consistency regularization has shown superiority in deep semi-supervised learning, which commonly estimates pseudo-label conditioned on each single sample and its perturbations. However, such a strategy ignores the relation between data points, and probably arises error accumulation problems once one sample and its perturbations are integrally misclassified. Against this issue, we propose Neighbor Matching, a pseudo-label estimator that propagates labels for unlabeled samples according to their neighboring ones (labeled samples with the same semantic category) during training in an online manner. Different from existing methods, for an unlabeled sample, our Neighbor Matching defines a mapping function that predicts its pseudo-label conditioned on itself and its local manifold. Concretely, the local manifold is constructed by a memory padding module that memorizes the embeddings and labels of labeled data across different mini-batches. We experiment with two distinct benchmark datasets for semi-supervised classification of thoracic disease and skin lesion, and the results demonstrate the superiority of our approach beyond other state-of-the-art methods. Source code is publicly available at https://github.com/renzhenwang/neighbor-matching.