Self-supervised learning (SSL) is a deep learning approach where models learn representations of data without relying on manually labeled datasets. Instead, the training process creates its own “labels” by leveraging the inherent structure or relationships within the input data. For example, a model might predict a missing part of an input (like a masked word in a sentence) or learn to relate different parts of the data (like predicting the next frame in a video). This contrasts with supervised learning, which requires explicit human-annotated labels, and unsupervised learning, which focuses on discovering patterns without any guidance.
A common way SSL works is by defining a “pretext task” that forces the model to learn useful features. In natural language processing (NLP), models like BERT are trained to predict masked words in sentences, using the surrounding context as both input and implicit labels. In computer vision, a model might be trained to predict the rotation angle of an image or reconstruct parts of an image that have been removed. These tasks don’t require human annotation because the labels are derived automatically from the data itself. Once the model learns these patterns, the learned features can be transferred to downstream tasks like classification or object detection, often requiring far fewer labeled examples for fine-tuning.
The key advantage of SSL is its ability to leverage vast amounts of unlabeled data, which is often more abundant than labeled datasets. For instance, training a model on millions of unlabeled images or text documents can yield a general-purpose feature extractor that performs well on specific tasks after minimal fine-tuning. This is particularly useful in domains like medical imaging or robotics, where labeling data is expensive or time-consuming. However, designing effective pretext tasks remains a challenge—poorly chosen tasks may not capture meaningful features. Despite this, SSL has become a cornerstone of modern AI systems, enabling breakthroughs in areas like language modeling (GPT, BERT) and vision (SimCLR, MAE), while reducing dependency on costly labeled datasets.