# A Survey on Semi-Supervised Learning Techniques

arXiv: https://arxiv.org/abs/1402.4645 · HTML: https://mlsanity.com/paper/1402.4645/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2014.

## Built on by (most cited first, 11 of 11)

- [SimMatch: Semi-supervised Learning with Similarity Matching](https://mlsanity.com/paper/2203.06915/) (2022)
- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [Graph Neural Networks: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2108.10733/) (2021)
- [A Review of Pseudo-Labeling for Computer Vision](https://mlsanity.com/paper/2408.07221/) (2024)
- [Semi-Supervised Learning with Multi-Head Co-Training](https://mlsanity.com/paper/2107.04795/) (2021)
- [VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media](https://mlsanity.com/paper/2110.15454/) (2021)
- [Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey](https://mlsanity.com/paper/2203.00190/) (2022)
- [Holistic Survey of Privacy and Fairness in Machine Learning](https://mlsanity.com/paper/2307.15838/) (2023)
- [Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer](https://mlsanity.com/paper/2407.08460/) (2024)
- [A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning](https://mlsanity.com/paper/2404.17978/) (2024)
- [Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)](https://mlsanity.com/paper/2206.14261/) (2022)

## Abstract (arXiv)

Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. There has been a large spectrum of ideas on semisupervised learning. In this paper we bring out some of the key approaches for semisupervised learning.