# A Review of Semi Supervised Learning Theories and Recent Advances

arXiv: https://arxiv.org/abs/1905.11590 · HTML: https://mlsanity.com/paper/1905.11590/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 1 of 1)

- [A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification](https://mlsanity.com/paper/2109.11834/) (2021)

## Abstract (arXiv)

Semi-supervised learning, which has emerged from the beginning of this century, is a new type of learning method between traditional supervised learning and unsupervised learning. The main idea of semi-supervised learning is to introduce unlabeled samples into the model training process to avoid performance (or model) degeneration due to insufficiency of labeled samples. Semi-supervised learning has been applied successfully in many fields. This paper reviews the development process and main theories of semi-supervised learning, as well as its recent advances and importance in solving real-world problems demonstrated by typical application examples.