- Semi-supervised learning combines both labelled and unlabelled data to improve learning efficiency, especially when labelled data is limited.
- It leverages the abundance of unlabelled data to enhance model performance and generalisation capabilities.
Semi-supervised learning is a middle ground between supervised and unsupervised learning. It uses a small amount of labelled data alongside a larger pool of unlabelled data to train machine learning models. The goal is to improve the learning process by making use of the unlabelled data to uncover underlying patterns and structures that are not evident from the labelled data alone. This approach helps in making more accurate predictions or classifications, particularly when labelled data is scarce or expensive to obtain.
Techniques in semi-supervised learning
Several techniques are employed in semi-supervised learning:
Self-training: This technique involves training a model on labelled data and then using the model to label the unlabelled data. The newly labelled data is then added to the training set, and the model is retrained iteratively.
Co-training: In co-training, two or more models are trained on different views or subsets of the data. Each model labels unlabelled data, and these labels are used to enhance the training of the other models.
Generative models: These models, such as gaussian mixture models (GMMs) or variational auto encoders (VAEs), learn the distribution of the data and can generate new examples. They can be used to improve the representation of both labelled and unlabelled data.
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Applications of semi-supervised learning
Semi-supervised learning is particularly useful in scenarios where obtaining labelled data is difficult or costly. For example:
Natural language processing: In NLP tasks like text classification or sentiment analysis, large amounts of text data are available, but only a small portion may be labelled. Semi-supervised learning helps in improving the accuracy of language models.
Image classification: In computer vision, semi-supervised learning can enhance models by using unlabelled images to improve classification performance when labelled images are limited.
Benefits and challenges
The main benefit of semi-supervised learning is its ability to leverage unlabelled data to improve model accuracy and generalisation. However, it also presents challenges, such as the potential for incorrect labels from the unlabelled data to introduce noise and affect model performance. Effective techniques and careful model evaluation are essential to maximise the benefits of semi-supervised learning.
