  Semi-Supervised Learning (SSL) algorithms have shown great potential in
training regimes when access to labeled data is scarce but access to unlabeled
data is plentiful. However, our experiments illustrate several shortcomings
that prior SSL algorithms suffer from. In particular, poor performance when
unlabeled and labeled data distributions differ. To address these observations,
we develop RealMix, which achieves state-of-the-art results on standard
benchmark datasets across different labeled and unlabeled set sizes while
overcoming the aforementioned challenges. Notably, RealMix achieves an error
rate of 9.79% on CIFAR10 with 250 labels and is the only SSL method tested able
to surpass baseline performance when there is significant mismatch in the
labeled and unlabeled data distributions. RealMix demonstrates how SSL can be
used in real world situations with limited access to both data and compute and
guides further research in SSL with practical applicability in mind.
