Self-supervised learning is a machine learning technique where models learn from unlabeled data by predicting missing or masked elements, such as a word or image patch, based on the surrounding context. It enables systems to train at scale without the cost of manual labeling, often producing powerful general-purpose models. Well-known models and frameworks that use self-supervised learning include GPT, BERT, and SimCLR.

It is closely related to supervised and unsupervised learning, which are two core approaches in machine learning. Supervised learning uses labeled data to train models, while unsupervised learning finds patterns in data without labels. Self-supervised learning bridges the two by automatically generating labels from raw data, offering a scalable and often more flexible way to pretrain models before fine-tuning them for specific tasks.

To develop a self-supervised model, researchers design a task where the model must predict a hidden or missing part of the input using the visible parts. As a result, the model learns meaningful internal representations of the data, which can then be used for downstream tasks like classification, summarization, or image recognition in domains such as finance, healthcare, or retail. 

How does self-supervised learning work?

Training a model using self-supervised learning requires following a number of carefully designed steps. The end goal is a system that can be adapted for real-world use. 

Here is an overview of how it works:

Data preparation and tokenization

Engineers collect large volumes of unlabeled data, such as text and images, from public sources. The data is cleaned to remove errors and duplicates as well as low-quality content. 

Once cleaned, it is broken down into smaller parts using a tokenizer. For language models, this means turning sentences into tokens the model can learn from. The process helps the model understand new or complex words by learning how frequently occurring subword units contribute to meaning. For image models, tokenization involves dividing pictures into small patches or pixel blocks.

Pretext tasks and pretraining

Self-supervised learning relies on pretext tasks — automatically created problems that teach the model to notice structure in the data. A common example in text is masking a word in a sentence and asking the model to predict what is missing. 

In image models, the system might apply random changes to an image and ask the model to recognize whether two versions are related. Because the original input is known, the system can check the model’s guesses and adjust the internal settings when errors are made.

This loop of prediction and correction continues across the entire dataset, allowing the model to learn useful patterns.

Transfer learning and fine-tuning

Once pretrained, the model can be adapted to solve a specific task. In some cases, it is used to generate useful features, such as detecting shapes or word patterns, which are then passed to a separate system to make a final decision. 

In other cases, the model is fine-tuned using a smaller labeled dataset. It continues training with a low learning rate so the model improves on the new task without forgetting earlier knowledge. 

Engineers use metrics such as accuracy or F1 score — which balances precision (how many results were correct) and recall (how many correct results were found) into a single score — to monitor performance and avoid overfitting, where the model becomes too tailored to the training data and struggles with new examples.

Deployment and monitoring

After fine-tuning, the model is prepared for real-world use. It is converted into an efficient format using tools such as ONNX or TorchScript and hosted on cloud services. Applications can access it through an API, sending input and receiving predictions. Engineers monitor metrics such as speed, accuracy, and fairness. If performance drops, they gather new data and update the model before releasing a new version.

Self-supervised learning use cases

Self-supervised learning is being used across a wide range of industries to train models directly on raw, unlabeled data. It is a transformative approach for solving enterprise problems where labeled data is limited or expensive to obtain.

Here are some of the ways self-supervised learning is being used in business contexts:

Medical imaging and clinical decision support

In healthcare, self-supervised learning models can analyze medical images like X-rays and CT scans to detect early signs of disease. Because high-quality labeled medical images are limited, self-supervised methods help models learn from large collections of unlabeled scans. 

The models can then perform well in tasks such as detecting tumors, supporting earlier and more accurate diagnosis. Separately, models trained on clinical notes using self-supervised techniques can flag symptoms or suggest possible diagnoses based on patterns found in patient records.

Search engines and chatbots

In natural language processing (NLP), self-supervised learning has become a core method for training language models that understand and generate text. Models like GPT are trained on large amounts of unlabeled text by predicting the next word in a sequence. They can then apply this understanding of user intent to specialized tasks such as powering search engines or answering customer questions.

eCommerce recommendations and review summarization

Online retailers use self-supervised learning to improve product recommendations by analyzing user behavior and product descriptions without needing manual labels. These models can also match similar items based on visual features, which helps customers discover products through image search. 

Another growing use case is review summarization, where models trained on large volumes of customer feedback automatically highlight key themes, saving shoppers time and helping businesses track sentiment around their products.