Self-supervised learning (SSL) and supervised learning differ primarily in how they obtain labeled data for training machine learning models. In supervised learning, models learn from explicitly labeled datasets where each input example is paired with a corresponding output label. For instance, an image classification model might be trained on photos labeled as “cat” or “dog.” The model’s goal is to map inputs to these predefined labels. In contrast, self-supervised learning generates labels automatically from the structure of unlabeled data, eliminating the need for manual annotation. SSL models create surrogate tasks—like predicting missing parts of the input—to learn meaningful representations of the data. For example, a language model might predict a masked word in a sentence, using the surrounding context as implicit labels.
The data requirements and use cases for each approach also differ. Supervised learning relies on large, curated datasets with high-quality labels, which can be time-consuming and costly to create. This makes it effective for well-defined tasks like object detection or sentiment analysis, where labeled data is available. SSL, however, leverages vast amounts of unlabeled data (e.g., text from books or unannotated images) by inventing tasks that turn the data’s inherent structure into supervision. A common SSL technique in computer vision involves training a model to predict the rotation angle of an image, forcing it to understand spatial relationships. This approach is particularly useful in domains where labeled data is scarce, such as medical imaging or multilingual translation, but raw data is abundant.
Finally, the training objectives and outcomes vary. Supervised models optimize for accuracy on specific labeled tasks, often resulting in narrow but highly tuned solutions. SSL models focus on learning general-purpose representations of the data, which can later be fine-tuned for multiple downstream tasks with minimal labeled examples. For example, a self-supervised language model like BERT learns contextual word embeddings by predicting masked tokens, and these embeddings can then be adapted for tasks like question answering or text summarization. This makes SSL a form of pre-training that reduces reliance on labeled data while enabling flexibility across applications. In contrast, supervised models are typically designed for a single task and require retraining if the task changes.