Self-supervised learning primarily involves training a model on what type of data?

Self-supervised learning primarily involves training a model on unlabeled data. In this approach, the model learns to generate labels from the data itself, allowing it to find patterns and representations without the need for explicitly labeled datasets. By utilizing techniques such as predicting parts of the input from other parts, or reconstructing the input data, the model can harness the vast amount of available unlabeled data.

This method is especially powerful because labeled data is often expensive and time-consuming to obtain. Self-supervised learning not only reduces the dependency on labeled datasets but also enhances the model's capability to generalize and understand the underlying structure of the data, making it particularly useful for applications in natural language processing, computer vision, and more.

While structured data refers to data organized in a defined manner, and time-series data indicates sequences of data points indexed in time order, the distinguishing feature of self-supervised learning is its reliance on unlabeled data directly.