MixMatch: A Holistic Approach to Semi-Supervised Learning

NeurIPS 2019  ·  David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel ·

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.

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data 		Result Benchmark
Image Classification CIFAR-10 MixMatch Percentage correct 95.05 # 137
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Image Classification CIFAR-100 MixMatch Percentage correct 74.1 # 152
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Semi-Supervised Image Classification CIFAR-10, 1000 Labels MixMatch Accuracy 92.25 # 1
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Semi-Supervised Image Classification CIFAR-10, 2000 Labels MixMatch Accuracy 92.97 # 1
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Semi-Supervised Image Classification CIFAR-10, 250 Labels MixMatch Percentage error 11.08 # 18
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Semi-Supervised Image Classification CIFAR-10, 4000 Labels MixMatch Percentage error 6.24 # 26
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Semi-Supervised Image Classification CIFAR-10, 500 Labels MixMatch Accuracy 91.35 # 1
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Image Classification STL-10 CutOut Percentage correct 87.36 # 46
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Image Classification STL-10 MixMatch Percentage correct 94.41 # 23
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Image Classification STL-10 IIC Percentage correct 88.80 # 42
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Semi-Supervised Image Classification STL-10, 1000 Labels MixMatch Accuracy 89.82 # 7
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Semi-Supervised Image Classification STL-10, 5000 Labels MixMatch Accuracy 94.41 # 1
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Image Classification SVHN MixMatch Percentage error 2.59 # 37
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Semi-Supervised Image Classification SVHN, 1000 labels MixMatch Accuracy 96.73 # 7
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Semi-Supervised Image Classification SVHN, 2000 Labels MixMatch Accuracy 96.96 # 1
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Semi-Supervised Image Classification SVHN, 250 Labels MixMatch Accuracy 96.22 # 8
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Semi-Supervised Image Classification SVHN, 4000 Labels MixMatch Accuracy 97.11 # 1
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Semi-Supervised Image Classification SVHN, 500 Labels MixMatch Accuracy 96.36 # 2
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