Self-supervised learning models learn from unlabeled data by using the data itself to create labels or tasks that help the model understand patterns and features in the data. This approach stands apart from traditional supervised learning, where models require a large amount of labeled data. In self-supervised learning, the model generates its own labels through various techniques, allowing it to derive useful representations without needing human-annotated examples.
For instance, a common approach is to use contrastive learning, where the model is trained to distinguish between similar and dissimilar examples. Suppose you have a collection of images. The model might randomly select pairs of images and create a task where it needs to identify which pairs belong to the same category versus which do not. By doing this over many iterations and with different image pairs, the model develops a better understanding of the underlying features that define distinct categories, such as color, shape, or texture.
Another popular method involves predicting parts of the data based on other parts. In natural language processing, for example, a self-supervised model might take a sentence with some words removed and attempt to predict the missing words. Similarly, in image processing, a model might learn to reconstruct an image from a corrupted version of itself. These tasks help the model learn rich representations of the data, making it easier to apply the learned knowledge in downstream tasks like classification or object detection, all without the need for extensive labeled datasets.