AI Scholar: A Holistic Approach to Semi-Supervised Learning

Source: Deep Learning on Medium


Semi-supervised learning has demonstrated that it is a powerful approach for leveraging unlabeled data to alleviate reliance on large labeled datasets. To that end, a group of Google researchers have put together leading semi-supervised approaches to come up with a new algorithm, MixMatch. MixMatch works by guessing low-entropy labels for data-augmented unlabeled examples and mixing labeled and unlabeled data using MixUp.

On evaluation, MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts. For instance, on CIFAR-10 with 250 labels, the error rate reduced from 38% to 11%) and by a factor of 2 on STL-10.

Potential Uses and Effects

MixMatch showed significantly improved performance compared to conventional methods. But there’s more to come, the researchers have got further interest in integrating additional ideas from semi-supervised learning literature into hybrid methods and continue to explore which components can result in more effective algorithms.

For now, MixMatch can help achieve dramatically better accuracy-privacy trade-off for differential privacy.

Read more: https://arxiv.org/abs/1905.02249v1

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