What is Self-Supervised Learning?
Self-supervised learning (SSL) is a paradigm in machine learning that aims to learn useful representations from unlabeled data, by exploiting the inherent structure or context of the data itself. Unlike supervised learning, which requires human-annotated labels for each data point, or unsupervised learning, which tries to discover hidden patterns or clusters in the data, self-supervised learning creates its own labels or objectives from the data, and uses them to train a model that can perform downstream tasks.
For example, imagine we have a large collection of images, but we do not know what objects they contain or what categories they belong to. Instead of manually labeling each image, we can use self-supervised learning to generate labels from the images themselves. One way to do this is to randomly mask out a part of each image, and then ask the model to predict what was in the masked region. This way, the model learns to recognize and reconstruct different parts of the image, and in the process, it also learns useful features that can be used for other tasks, such as object detection or classification.