Introduction to Semi-supervised Learning

Machine Learning is a subset of Artificial Intelligence that allows systems to learn from their past performance without having to be explicitly programmed. The whole ML relies on input, such as training data or graphs, to grasp things and their connections, similar to how the human brain acquires information and understanding.

In order to later draw conclusions based on the examples given, ML models search for patterns in the data. The main goal of ML is to make it possible for computers to learn on their own, without human aid, and to adapt their behavior accordingly. There are mainly four types of machine learning: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning.

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Summary

In this blog, our main focus is to cover the details about semi-supervised learning, its importance and how can you make use of it in the real world. But before proceeding, let’s learn a brief about types of machine learning to set up the base for semi-supervised learning.

Semi-supervised Learning

The idea of semi-supervised learning is presented to address these issues with supervised learning and unsupervised learning methods. The training set for this algorithm consists of both labeled and unlabeled data. While there is a significant amount of unlabeled data, there is a relatively little amount of annotated data.

Why is Semi-supervised learning important?

• You can utilize semi-supervised techniques to increase the quantity of your training data when you don’t have enough labeled data to create an accurate model and you don’t have the skills or resources to obtain more data. 
• To address the major issues with supervised learning and unsupervised learning methodologies, semi-supervised learning serves as a bridge. 
• With it, you initially train a model on a small sample of labeled data before applying it repeatedly to a larger sample of unlabeled data. It is effective for a wide range of issues, including clustering, association, and regression as well as classification. 
• It also cuts down on the time it takes to prepare your data.

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How does semi-supervised learning work?

• You choose a small sample of labeled data, such as pictures of cats and dogs with their corresponding tags, and you utilize this dataset to train a base model using conventional supervised learning techniques.
• Then, using the partially trained model to create predictions for the remaining data that has not yet been labeled, you apply the pseudo-labeling technique. The labels created later are referred to as “pseudo labels” because they were created using the first labeled data, which had restrictions.
• There may be numerous iterations of the process, with new pseudo-labels being added each time. The model’s performance will continue to improve with each iteration assuming that the data is appropriate for the procedure.
• There may be numerous iterations of the process, with new pseudo-labels being added each time. The model’s performance will continue to improve with each iteration assuming that the data is appropriate for the procedure.

Assumptions made by semi-supervised learning

• The method assumes that points that are near one another are more likely to have the same output label.
• The data can be organized into discrete clusters, and points within a cluster are more likely to have the same output label than those outside of it.
• Manifold Assumption: The data roughly correspond to a manifold with a dimension significantly lower than the input space. The usage of distances and densities that are defined on a manifold is permitted under this presumption.

Types of Semi-supervised Learning

• Inductive Semi-supervised learning: What we typically refer to as classical supervised learning is the same as inductive learning. Based on an existing labeled training dataset, we construct and train a machine learning model. The labels of a testing dataset that we have never seen before are then predicted using this trained model.

• Transductive semi-supervised learning: The training and testing datasets have both been observed in advance via transductive learning techniques. To forecast the labels of the testing dataset, we first learn from the training dataset that has previously been seen. We can use the patterns and other information included in these data during the learning process even when we are unaware of the labels of the testing datasets.

Is Semi-supervised learning better than the other two machine learning approaches?

When utilizing supervised learning to create (usually predictive) models, you are working with a labeled dataset. Unsupervised learning frequently has an exploratory (clustering, compression) focus when working with unlabeled data.

In semi-supervised learning, labeled data is enhanced by (usually a small quantity of) unlabeled data to help solve a supervised learning problem. Therefore, the objective is to solve one of the supervised learning’s issues: a lack of labeled data. You are expecting to create a better model by including inexpensive and plentiful unlabeled data in addition to supervised learning alone.

So, semi-supervised learning appears to be powerful but you should be attentive and must have done a good analysis to make sure that your data is compatible with semi-supervised learning. The method might not work if the labeled data’s sample isn’t indicative of the distribution as a whole.

Applications of Semi-supervised learning

1. On the internet, there are countless websites with a variety of classified content. A large human resource pool with the ability to arrange and categorize the content on the web pages is necessary to make this information accessible to web users. By identifying and labeling the material, semi-supervised learning can aid in enhancing the user experience. A semi-supervised learning model is used by several search engines, including Google, to categorize and rank web pages in search results.
2. One of the most popular applications of semi-supervised learning models is the analysis of images and audio. Usually, this kind of data is unlabeled. Instead of classifying every image or audio file for a specific field over days or months, a small fraction of the data can be labeled by humans with experience. Once this little sample of the data has been classified, you can easily classify the rest of the data you have by using the trained algorithm.
3. Because labeling audio requires a lot of time and resources, semi-supervised learning can be utilized to overcome these obstacles and deliver superior results. Meta has successfully enhanced its voice recognition models using semi-supervised learning. They began with the base model, which was developed using approximately 100 hours of audio data annotated by humans. The performance of the models was then improved via self-training and the addition of around 500 hours of unlabeled voice data.

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Advantages of Semi-supervised learning

• It is simple to comprehend.
• Semi-supervised learning is powerful when labels are limited and unlabeled data is plentiful.
• Your model’s performance and generalization can be enhanced. Without spending time and money classifying tens of thousands of additional photos, your model gets exposure to situations it might see during deployment.
• In innumerable circumstances, labeled data is not easily accessible. With only a small portion of the labeled data, semi-supervised learning can complete typical tasks with state-of-the-art outcomes.
• From crawling engines and information aggregation systems to picture and speech recognition, semi-supervised learning is used everywhere.

Disadvantages of Semi-supervised

• The outcomes of iterations are unstable.
• Data at the network level is not applicable for semi-supervised learning.
• Because there is no method to confirm that the algorithm has generated labels that are 100% accurate, it produces less reliable results than conventional supervised procedures.

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Conclusion

The benefits and drawbacks of supervised and unsupervised learning are very well balanced by semi-supervised learning. Additionally, it guarantees that a significant amount of generated or accessible data can be included in one model or the other to produce insightful results. The creation of a dataset is the most challenging machine learning challenge. Unlabeled data is less expensive than labeled data. Semi-supervised learning incorporates both kinds of data. However, this does not imply that semi-supervised learning can be used for all problems. The method might not work if the labeled data’s sample isn’t indicative of the distribution as a whole.