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:

Thanks for reading. Please comment, share and remember to subscribe to our weekly AI Scholar Newsletter for the most recent and interesting research papers! You can also follow me on Twitter and LinkedIn. Remember to 👏 if you enjoyed this article. Cheers!