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.
CITATION STYLE
Cicek, S., Fawzi, A., & Soatto, S. (2018). SaaS: Speed as a Supervisor for Semi-supervised Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11206 LNCS, pp. 152–166). Springer Verlag. https://doi.org/10.1007/978-3-030-01216-8_10
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