  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.
