Pushing the limit of semi-supervised learning with the Unified Semi-supervised Learning Benchmark - Microsoft Research
![Pushing the limit of semi-supervised learning with the Unified Semi-supervised Learning Benchmark - Microsoft Research](https://www.microsoft.com/en-us/research/uploads/prod/2022/09/usb-3-1024x698.png)
Neural models give competitive results when trained with supervised learning using sufficient high-quality labeled data. For example, according to statistics from the Paperswithcode website, recent traditional supervised learning methods can achieve an accuracy of over 88% on the ImageNet dataset, which contains millions of data. However, acquiring large amounts of labeled data is often time-consuming […]
![](https://akyrillidis.github.io/public/ssl_toyexample.png)
Semi-supervised learning (SSL) - A systematic survey — Anastasios Kyrillidis
![](https://miro.medium.com/v2/resize:fit:1358/1*7zwex3hagHMjAl34ps0FxA.png)
Unveiling the Power of Semi-Supervised Learning: The Unified Semi-Supervised Learning Benchmark, by Jindong Wang, PyTorch
![](https://media.springernature.com/m685/springer-static/image/art%3A10.1186%2Fs40537-021-00419-9/MediaObjects/40537_2021_419_Fig6_HTML.png)
A survey on data‐efficient algorithms in big data era, Journal of Big Data
![](https://www.researchgate.net/publication/277605013/figure/fig4/AS:281234816159750@1444063013959/Principle-of-semi-supervised-learning-1-a-model-eg-CSP-LDA-classifier-is-first.png)
Principle of semi-supervised learning: 1) a model (e.g., CSP+LDA
Advancing Biosensors with Machine Learning
![](https://www.microsoft.com/en-us/research/uploads/prod/2022/09/usb-4-1024x381.png)
Pushing the limit of semi-supervised learning with the Unified Semi-supervised Learning Benchmark - Microsoft Research
![](https://miro.medium.com/v2/resize:fit:1400/1*CzVENZj3bWrwhRBN4hQq7Q.png)
Beginner Guide of Semi-Supervised Learning
![](https://media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-031-19821-2_4/MediaObjects/540000_1_En_4_Fig1_HTML.png)
CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation
![](https://i0.wp.com/syncedreview.com/wp-content/uploads/2020/06/Screenshot-2020-06-20-18.17.21.png?fit=1071%2C581&ssl=1)
Google Brain's SimCLRv2 Achieves New SOTA in Semi-Supervised Learning