We propose using active learning based techniques to further improve the state-of-the-art semi-supervised learning MixMatch algorithm. We provide a thorough empirical evaluation of several active-learning and baseline methods, which successfully demonstrate a significant improvement on the benchmark CIFAR-10, CIFAR-100, and SVHN datasets (as much as 1.5% in absolute accuracy). We also provide an empirical analysis of the cost trade-off between incrementally gathering more labeled versus unlabeled data. This analysis can be used to measure the relative value of labeled/unlabeled data at different points of the learning curve, where we find that although the incremental value of labeled data can be as much as 20x that of unlabeled, it quickly diminishes to less than 3x once more than 2,000 labeled example are observed. Code can be found at https://github.com/google-research/mma.

使用基于主动学习技术的方法改进了半监督学习 MixMatch 算法，并在 CIFAR-10、CIFAR-100 和 SVHN 数据集上进行了全面的实证评估，得到了显著的性能提升。此外，还对标记和非标记数据成本之间的权衡进行了实证分析，发现虽然有标记数据的增量效益可以高达非标记数据的 20 倍，但一旦观察到超过 2000 个标记示例，其效益就会迅速减弱到不到 3 倍。

结合 MixMatch 和主动学习以在更少的标签下实现更高准确度