Semi-supervised learning is a machine learning technique that combines a small amount of labeled data with a large amount of unlabeled data to improve model performance. This approach is particularly valuable in situations where obtaining labeled data is costly or time-intensive, such as in medical imaging or natural language processing. By leveraging the patterns in unlabeled data, semi-supervised learning can achieve performance close to fully supervised methods while significantly reducing the need for extensive labeling. Techniques such as self-training and co-training are commonly used to iteratively label the unlabeled data and improve the model's predictive accuracy.
https://en.wikipedia.org/wiki/Semi-supervised_learning
One of the key approaches in semi-supervised learning involves graph-based methods, where data points are treated as nodes in a graph, and their connections represent similarities. The algorithm propagates label information from labeled to unlabeled nodes, allowing the model to learn from the structure of the data. Other approaches, like generative models and autoencoders, leverage the latent structure of the data to enhance learning. Semi-supervised learning is widely used in applications such as speech recognition, web content classification, and fraud detection, where fully labeled datasets are scarce.
https://www.turing.ac.uk/research/research-projects/semi-supervised-learning
Advancements in semi-supervised learning have been driven by the integration of deep learning techniques, particularly in fields like image recognition and text classification. Methods such as MixMatch, FixMatch, and Pseudo-Labeling have emerged to refine semi-supervised learning workflows by reducing noise and improving data utilization. These techniques, combined with modern frameworks like PyTorch and TensorFlow, have made semi-supervised learning a practical and efficient solution for addressing real-world machine learning challenges.