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Semi-supervised learning (SSL) is a key approach toward more data-efficientmachine learning by jointly leverage both labeled and unlabeled data. Wepropose AlphaMatch, an efficient SSL method that leverages data augmentations,by efficiently enforcing the label consistency between the data points and theaugmented data derived from them. Our key technical contribution lies on: 1)using alpha-divergence to prioritize the regularization on data with highconfidence, achieving a similar effect as FixMatch but in a more flexiblefashion, and 2) proposing an optimization-based, EM-like algorithm to enforcethe consistency, which enjoys better convergence than iterative regularizationprocedures used in recent SSL methods such as FixMatch, UDA, and MixMatch.AlphaMatch is simple and easy to implement, and consistently outperforms priorarts on standard benchmarks, e.g. CIFAR-10, SVHN, CIFAR-100, STL-10.Specifically, we achieve 91.3% test accuracy on CIFAR-10 with just 4 labelleddata per class, substantially improving over the previously best 88.7% accuracyachieved by FixMatch.

AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence