Self-supervised learning and unsupervised learning are two different approaches to training machine learning models, and the distinction lies primarily in how they use data. In unsupervised learning, models are trained on data without any labeled outputs, meaning the model learns to identify patterns or structures within the data based solely on its inherent properties. For example, clustering algorithms like K-means or hierarchical clustering analyze data points and group them based on their similarities without any prior labels to guide them.
In contrast, self-supervised learning is a special category of unsupervised learning where the model generates its own labels from the input data. This process typically involves creating auxiliary tasks that can help the model learn useful representations of the data. For instance, in image classification, a model might take an image and mask parts of it, then train itself to predict the missing parts based on the context provided by the unmasked portions. This way, the model learns to understand the features and relationships in the data, making it more effective for downstream tasks.
The key takeaway is that while both methods rely on unlabelled data, they differ in how they approach the learning process. Unsupervised learning focuses on identifying underlying structures, while self-supervised learning constructs its own tasks to facilitate learning. This distinction not only influences the training dynamics but also affects the applicability of the models; self-supervised learning often leads to better performance on tasks that require a deep understanding of the data's context, such as natural language processing and computer vision.