Self-Supervised Learning (SSL)
Self-supervised learning (SSL) is an approach to machine learning that relies on automatically generated labels derived from the data itself as opposed to manual annotation. This is done through the creation of pretext tasks that serve as proxies for the actual task and can be used to train the model to predict patterns and data.
For example, an SSL model might be trained to predict the next word in a sentence or the missing part of an image. By solving these tasks, the model learns features that can be fine-tuned for specific applications later down the line.
Predictive learning
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SSL
Examples
Let’s imagine that you want to make an AI model that recognises cats in any given image. When building a supervised model, this would be done by having a person annotate hundreds or thousands of images to detail whether a cat was or wasn’t in the image. This can be quite expensive and time consuming.
For an SSL model on the other hand, the model is first trained on a large set of unlabelled images, which may include cats, dogs, and other things. The model would then automatically modify the images, changing the color, rotating them, taking out pieces of the image and so on. The model will see two differently modified versions of the same image and is trained to understand that they represent the same object.
This teaches the model to focus on the features that define the object (e.g., a cat’s shape, texture, ears), not superficial differences like lighting or orientation. By doing so, it will learn the intricacies of what makes a cat a cat.
Once the model has learned useful image features from unlabelled data, it can be fine-tuned using a much smaller labelled dataset. After that, it can accurately classify new images as “cat” or “not cat”, often with far fewer labelled examples than a supervised model would require.
FAQ
What are the advantages of using SSL?
SSL reduces the reliance on labeled data, which can be expensive and time-consuming to get. It allows models to effectively use large volumes of unlabeled data, leading to improved performance and lower operational costs.
How does SSL differ from unsupervised learning?
While both SSL and unsupervised learning deal with unlabeled data, SSL involves creating surrogate labels from the data itself to train models on specific tasks, while unsupervised learning models focus on uncovering hidden patterns or structures without any labels.
Can SSL be applied to domains beyond natural language processing?
Yes, SSL has been successfully applied in various domains, including video, audio and images.
