How semi-supervised learning is reinventing the training of AI models
Not long ago we were talking about supervised and unsupervised learning in one of our articles... it's time to talk about semi-supervised learning, which is at the crossroads between supervised and unsupervised methods, offering a promising solution for maximizing the effectiveness of artificial intelligence (AI) models while minimizing the need for labeled data... without making it obsolete!
This approach takes advantage of a small portion of annotated data, while exploiting a large volume of unlabeled data, in order to improve the accuracy and performance of machine learning algorithms.
In a context where manual data annotation is a challenge in terms of cost and time, semi-supervised learning is distinguished by its ability to bridge this gap and open up new perspectives for AI, especially in areas such as Computer Vision and natural language processing.
This paradigm is based on several key principles, including the continuity hypothesis and the clustering hypothesis, which allow model predictions to be adjusted based on the similarities observed between labeled and unlabeled data.
Techniques like pseudo-labeling and consistency regularization also play a major role in this approach, promoting the creation of robust models even when annotated data is scarce.
In summary, we tell you everything about this method in this article! Before we begin, however, we would like to remind you that creating datasets is essential, and that the use of semi-supervised learning does not eliminate the need for manually annotated and verified data. On the contrary, this approach makes it possible to focus on workflows more qualitative certifications, more technical and more precise, in order to produce data sets that are likely to be smaller, but more 🎯precise, more 🧾Complete and more 🦺reliable.
Introduction to semi-supervised learning
Semi-supervised learning is a machine learning technique that combines the benefits of supervised and unsupervised learning. This method reduces the cost and time required to collect labeled data, while improving the generalization of machine learning models. In this article, we will explore the principles and applications of semi-supervised learning, as well as the tools and techniques used to implement this method.
Semi-supervised learning is characterized by its ability to use a set of partially labeled data. Unlike supervised learning, which relies only on labeled data, and unsupervised learning, which only uses unlabeled data, semi-supervised learning uses both types of data to train more robust and efficient models.
A concrete example of this method is co-learning, where two classifiers learn from the same set of data using different characteristics. For example, to classify individuals into men and women, a classifier could use size while another would use hairiness. This approach makes it possible to maximize the use of available data and to improve the accuracy of the models.
The algorithms of Machine Learning such as neural networks, decision trees, and algorithms of clustering are commonly used in semi-supervised learning. In addition, data processing techniques such as standardization, variable selection, and information suppression are essential for improving data quality and, therefore, model performance.
Semi-supervised learning has applications in a variety of fields, including image recognition, speech recognition, text classification, and time series forecasting. In healthcare, for example, this method is used to analyze medical images and predict diagnoses with a limited amount of labeled data. Likewise, in finance, it helps to detect fraud by exploiting partially labeled transactions.
💡 In summary, semi-supervised learning is a powerful method that combines the benefits of supervised and unsupervised learning. By reducing the need for labelled data and improving the generalization of models, this technique offers an effective solution for analyzing and predicting complex data in various fields.
What is semi-supervised learning?
Semi-supervised learning is a machine learning method that combines a small set of labelled data with a large volume of unlabelled data to train a model.
This approach is particularly useful when annotating data is expensive or difficult to perform, but there is a large amount of unlabeled raw data. It falls between supervised learning (which relies only on labeled data) and unsupervised learning (which does not rely on any labeled data). In this context, each data sample is associated with a specific class in order to properly classify the data.
The fundamental principle of semi-supervised learning is based on two important assumptions:
- The continuity hypothesis : Data points that are close to each other in the feature space are more likely to have the same label. In other words, similar data should share similar labels.
- The grouping hypothesis : data tends to cluster naturally around Clusters distinct, and these groupings can be used to help assign labels to unlabeled data.
Techniques such as pseudo-labeling, where the model generates labels for unlabeled data based on its predictions, and consistency regularization, which encourages stable predictions between labeled and unlabeled examples, are often used to improve the performance of semi-supervised learning models.
How does it differ from supervised and unsupervised methods?
Semi-supervised learning differs from supervised and unsupervised methods in the way in which data is used to train models.
Supervised learning
In this approach, all of the data used to train the model is labeled, forming a data set where each example is associated with a correct answer or label. The model learns by comparing its predictions with these labels to adjust its parameters.
Supervised learning is very effective when large amounts of labeled data are available, but it becomes limited when manually annotating data is expensive or difficult.
Unsupervised learning
Unlike supervised learning, unsupervised learning does not use any labeled data. The model attempts to find underlying structures in the data, such as groups or patterns. Unsupervised algorithms are often used for tasks like clustering or the dimensionality reduction.
However, this method does not allow labels to be directly associated with data, which limits its use for classification or prediction tasks.
Semi-supervised learning
Semi-supervised learning combines both approaches. It relies on a small set of labeled data, which guides model learning, while exploiting a large amount of unlabeled data to improve generalization and performance.
This method reduces the dependence on fully annotated data and allows the model to learn from the structure of unlabeled data while relying on labeled examples to refine predictions.
How does semi-supervised learning improve the effectiveness of AI models?
Semi-supervised learning improves the effectiveness of artificial intelligence (AI) models in a number of ways, combining the benefits of both supervised and unsupervised methods.
Use of unlabeled data
In many cases, obtaining labelled data is expensive and time consuming. Semi-supervised learning makes it possible to take advantage of a large amount of unlabeled data, which is often easier to obtain, while using a small set of labeled data to guide model learning.
This makes it possible to improve the generalization of the model without requiring a massive amount of labeled data, thus reducing annotation time and cost.
Improving generalization
Models trained on a small set of labeled data are often subject to overtraining (overfitting), where the model learns too specifically from the labeled examples and doesn't generalize well to new data.
By integrating unlabeled data, semi-supervised learning allows the model to learn underlying relationships and structures in the data, improving its ability to generalize to examples not seen.
Regularization by consistency
A common technique in semi-supervised learning is consistency regularization, where the model is encouraged to produce stable predictions for similar data, regardless of whether it is labeled or not. This reinforces the robustness of the model by making predictions more consistent, even for minor variations in the data.
Pseudo-labeling
This technique consists in using the model to generate labels on the unlabeled data, based on its predictions. These pseudo-labels are then used to train the model in a manner similar to the labelled data. This allows the model to train on a larger volume of data while taking advantage of the information available in the unlabeled data.
Reducing the need for labelled data
Semi-supervised learning makes it possible to significantly reduce the amount of labeled data required to obtain a performance similar to or greater than that obtained with purely supervised methods. This makes it particularly suitable for scenarios where labelling resources are limited, such as in specialized fields (for example, medicine or science).
In what areas is semi-supervised learning most used?
Semi-supervised learning is used in many areas where access to labeled data is limited, but where a large amount of unlabeled data is available. Here are some of the most important areas where this method is particularly useful:
1. Computer Vision
Semi-supervised learning is widely used for tasks such as image classification, object detection and image segmentation. Image recognition systems, especially in the medical field (X-ray analysis, MRI), video surveillance, and autonomous driving, benefit greatly from this approach. These systems often require large amounts of data, but the high cost of manually tagging images makes semi-supervised learning very appealing.
2. Natural language processing (NLP)
In language processing, such as text classification, sentiment analysis, or machine translation, semi-supervised learning makes it possible to process large volumes of unlabeled text. This approach is particularly useful for tasks like information extraction, where it can be difficult to obtain fully labelled data sets.
3. Voice recognition
Voice recognition systems, such as virtual assistants (Siri, Alexa, etc.), often use semi-supervised models to process unlabeled audio samples. Voice recognition requires a large amount of labeled audio data, but the acquisition of these labels is expensive and time consuming. Semi-supervised therefore makes it possible to take advantage of unlabeled audio data to improve the performance of these systems.
4. Medicine and medical imaging
In the medical field, the annotation of data is particularly difficult due to the specialization required. Semi-supervised models are used for the analysis of medical images (x-rays, scanners), allowing diseases to be automatically diagnosed while minimizing the amount of labelled data required.
5. Bioinformatics
Semi-supervised learning is also used for the analysis of genomic, proteomic, and other biological data. In these areas, where accurate data labeling is often limited due to the complexity and cost of research, this approach makes it possible to better exploit the vast amounts of unlabeled data available.
6. Fraud detection
Fraud detection systems, used in finance or online transactions, can also benefit from semi-supervised learning. In these systems, a small portion of transactions may be labeled as fraudulent or legitimate, while the majority of transactions remain unlabeled. Semi-supervised learning helps identify hidden patterns in this unlabeled data to improve detection.
Conclusion
Semi-supervised learning offers a balanced and effective approach to training AI models by exploiting labelled and unlabelled data. This method reduces annotation costs while improving the performance and generalization of models.
Its application in various fields, such as computer vision, natural language processing, and medicine, is a testament to its ability to meet the challenges posed by the limited availability of labeled data. By combining flexibility and efficiency, semi-supervised learning is therefore a key solution for optimizing artificial intelligence systems in the future!
