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MixMatch: A Holistic Approach to Semi-Supervised Learning

David Berthelot
Nicholas Carlini
Ian Goodfellow
Nicolas Papernot
Avital Oliver
Colin Raffel
NeurIPS (2019) (to appear)


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 called MixMatch. MixMatch works by guessing low-entropy la- bels for data-augmented unlabeled examples, and then mixes 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. 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.

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