Title: Smooth Neighbors on Teacher Graphs for Semi-supervised Learning,
Abstract: The recently proposed self-ensembling methods have achieved promising results
in deep semi-supervised learning, which penalize inconsistent predictions of
unlabeled data under different perturbations. However, they only consider
adding perturbations to each single data point, while ignoring the connections
between data samples. In this paper, we propose a novel method, called Smooth
Neighbors on Teacher Graphs (SNTG). In SNTG, a graph is constructed based on
the predictions of the teacher model, i.e., the implicit self-ensemble of
models. Then the graph serves as a similarity measure with respect to which the
representations of "similar" neighboring points are learned to be smooth on the
low-dimensional manifold. We achieve state-of-the-art results on
semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for
CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular,
the improvements are significant when the labels are fewer. For the
non-augmented MNIST with only 20 labels, the error rate is reduced from
previous 4.81% to 1.36%. Our method also shows robustness to noisy labels.