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Poster
MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot · Nicholas Carlini · Ian Goodfellow · Nicolas Papernot · Avital Oliver · Colin A Raffel

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #32
in Algorithms -- Semi-Supervised Learning »

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 guesses low-entropy labels for data-augmented unlabeled examples and mixes labeled and unlabeled data using MixUp. 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. Code is attached.

Keywords: Semi-Supervised Learning · Classification · Algorithms
[ Paper]  [ Poster] 

Author Information

David Berthelot (Google Brain)
Nicholas Carlini (Google)
Ian Goodfellow (Google Brain)
Nicolas Papernot (University of Toronto)
Avital Oliver (Google Brain)
Colin A Raffel (Google Brain)

My research focuses on machine learning techniques for sequential data. I am currently a resident at Google Brain. I recently completed a PhD in Electrical Engineering at Columbia University In LabROSA, supervised by Dan Ellis. My thesis focused on learning-based methods for comparing sequences. In 2010, I received a Master's in Music, Science and Technology from Stanford University's CCRMA, supervised by Julius O. Smith III. I did my undergrad at Oberlin College, where I majored in Mathematics.

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