MixMatch: A Holistic Approach to Semi-Supervised Learning

David Berthelot
Ian Goodfellow
Avital Oliver
Colin Raffel
NeurIPS (2019) (to appear)

Abstract

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.

Research Areas