Abstract. We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a
deep neural network to measure the quality of an iterative estimate of
the posterior probability of unknown labels. Training speed in supervised
learning correlates strongly with the percentage of correct labels, so we
use it as an inference criterion for the unknown labels, without attempting to infer the model parameters at first. Despite its simplicity, SaaS
achieves competitive results in semi-supervised learning benchmarks