论文标题
Alphamatch：通过alpha-Divermence提高半监督学习的一致性
AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence
论文作者
论文摘要
半监督学习（SSL）是通过共同利用标记和未标记数据的共同利用数据效率的机器学习的关键方法。我们提出了Alphamatch，这是一种有效的SSL方法，该方法通过有效地在数据点和从中得出的增强数据之间的标签一致性来利用数据增强。 Our key technical contribution lies on: 1) using alpha-divergence to prioritize the regularization on data with high confidence, achieving a similar effect as FixMatch but in a more flexible fashion, and 2) proposing an optimization-based, EM-like algorithm to enforce the consistency, which enjoys better convergence than iterative regularization procedures used in recent SSL methods such as FixMatch, UDA, and MixMatch. Alphamatch简单易于实现，并且在标准基准测试中始终优于先前的艺术，例如CIFAR-10，SVHN，CIFAR-100，STL-10。具体而言，我们在CIFAR-10上实现了91.3％的测试准确性，每个类别只有4个标记的数据，从而显着提高了FixMatch实现的先前最佳88.7％精度。
Semi-supervised learning (SSL) is a key approach toward more data-efficient machine learning by jointly leverage both labeled and unlabeled data. We propose AlphaMatch, an efficient SSL method that leverages data augmentations, by efficiently enforcing the label consistency between the data points and the augmented data derived from them. Our key technical contribution lies on: 1) using alpha-divergence to prioritize the regularization on data with high confidence, achieving a similar effect as FixMatch but in a more flexible fashion, and 2) proposing an optimization-based, EM-like algorithm to enforce the consistency, which enjoys better convergence than iterative regularization procedures used in recent SSL methods such as FixMatch, UDA, and MixMatch. AlphaMatch is simple and easy to implement, and consistently outperforms prior arts 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 labelled data per class, substantially improving over the previously best 88.7% accuracy achieved by FixMatch.
