Semi-supervised learning has proven to be a powerful paradigm for leveraging
unlabeled data to mitigate the reliance on large labeled datasets. In this
work, we unify the current dominant approaches for semi-supervised learning to
produce a new algorithm, MixMatch, that works by guessing low-entropy labels
for data-augmented unlabeled examples and mixing labeled and unlabeled data
using MixUp. We show that MixMatch obtains state-of-the-art results by a large
margin across many datasets and labeled data amounts. For example, on CIFAR-10
with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by
a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a
dramatically better accuracy-privacy trade-off for differential privacy.
Finally, we perform an ablation study to tease apart which components of
MixMatch are most important for its success.

本研究提出了一种半监督学习算法 MixMatch，采用猜测低熵标签的方法，通过使用 MixUp 混合标记和未标记的数据来处理扩充后的未标注样例。实验结果表明，MixMatch 在许多数据集和标记数据量上都取得了大幅度的优越结果，同时也证明了 MixMatch 如何帮助实现更好的准确性和隐私权的权衡。最终，我们进行了消融研究来分离 MixMatch 的哪些组件对其成功最为重要。